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Abbiw-Jackson, Roselyn Mansa. "Discrete optimization models in data visualization." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1987.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.Thesis research directed by: Applied Mathematics and Scientific Computation. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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MacDonald, Iain L. "Time series models for discrete data." Doctoral thesis, University of Cape Town, 1992. http://hdl.handle.net/11427/26105.
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McElduff, F. C. "Models for discrete epidemiological and clinical data." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1348493/.
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Discrete data, often known as frequency or count data, comprises of observations which can only take certain separate values, resulting in a more restricted numerical measurement than those provided by continuous data and are common in the clinical sciences and epidemiology. The Poisson distribution is the simplest and most common probability model for discrete data with observations assumed to have a constant rate of occurrence amongst individual units with the property of equal mean and variance. However, in many applications the variance is greater than the mean and overdispersion is said to be present. The application of the Poisson distribution to data exhibiting overdispersion can lead to incorrect inferences and/or inefficient analyses. The most commonly used extension of the Poisson distribution is the negative binomial distribution which allows for unequal mean and variance, but may still be inadequate to model datasets with long tails and/or value-inflation. Further extensions such as Delaporte, Sichel, Gegenbauer and Hermite distributions, give greater flexibility than the negative binomial distribution. These models have received less interest than the Poisson and negative binomial distributions within the statistical literature and many have not been implemented in current statistical software. Also, diagnostics and goodness-of-fit statistics are seldom considered when analysing such datasets. The aim of this thesis is to develop software for analysing discrete data which do not follow the Poisson or negative binomial distributions including component-mix and parameter-mix distributions, value-inflated models, as well as modifications for truncated distributions. The project’s main goals are to create three libraries within the framework of the R project for statistical computing. They are: 1. altmann: to fit and compare a wide range of univariate discrete models 2. discrete.diag: to provide goodness-of-fit and outlier detection diagnostics for these models 3. discrete.reg: to fit regression models to discrete response variables within the gamlss framework These libraries will be freely available to the clinical and scientific community to facilitate discrete data interpretation.
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Plan, Elodie L. "Pharmacometric Methods and Novel Models for Discrete Data." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-150929.
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Pharmacodynamic processes and disease progression are increasingly characterized with pharmacometric models. However, modelling options for discrete-type responses remain limited, although these response variables are commonly encountered clinical endpoints. Types of data defined as discrete data are generally ordinal, e.g. symptom severity, count, i.e. event frequency, and time-to-event, i.e. event occurrence. Underlying assumptions accompanying discrete data models need investigation and possibly adaptations in order to expand their use. Moreover, because these models are highly non-linear, estimation with linearization-based maximum likelihood methods may be biased. The aim of this thesis was to explore pharmacometric methods and novel models for discrete data through (i) the investigation of benefits of treating discrete data with different modelling approaches, (ii) evaluations of the performance of several estimation methods for discrete models, and (iii) the development of novel models for the handling of complex discrete data recorded during (pre-)clinical studies. A simulation study indicated that approaches such as a truncated Poisson model and a logit-transformed continuous model were adequate for treating ordinal data ranked on a 0-10 scale. Features that handled serial correlation and underdispersion were developed for the models to subsequently fit real pain scores. The performance of nine estimation methods was studied for dose-response continuous models. Other types of serially correlated count models were studied for the analysis of overdispersed data represented by the number of epilepsy seizures per day. For these types of models, the commonly used Laplace estimation method presented a bias, whereas the adaptive Gaussian quadrature method did not. Count models were also compared to repeated time-to-event models when the exact time of gastroesophageal symptom occurrence was known. Two new model structures handling repeated time-to-categorical events, i.e. events with an ordinal severity aspect, were introduced. Laplace and two expectation-maximisation estimation methods were found to be performing well for frequent repeated time-to-event models. In conclusion, this thesis presents approaches, estimation methods, and diagnostics adapted for treating discrete data. Novel models and diagnostics were developed when lacking and applied to biological observations.
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Elgmati, Entisar. "Additive intensity models for discrete time recurrent event data." Thesis, University of Newcastle Upon Tyne, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556142.
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The thesis considers the Aalen additive regression model for recurrent event data. The model itself, estimation of the cumulative regression functions, testing procedures, checking goodness of fit and inclusion of dynamic covariates in the model are reviewed. A disadvantage of this model is that estimates of the conditional probabilities are not constrained to lie between zero and one, therefore a model with logistic intensity is considered. Results under the logistic model are shown to be qualitatively similar to those under the additive model. The additive model is extended to incorporate the possibility of spatial or spatio-temporal clustering, possibly caused by unobserved environmental factors or infectivity. Various tests for the presence of clustering are described and implemented. The issue of frailty modelling and its connection to dynamic modelling is presented and examined. We show that frailty and dynamic models are almost indistinguishable in terms of residual summary plots. A graphical procedure based on the property that the covariance between martingale residuals at time to and t > to is independent of t is proposed and supplemented by a formal test statistic to investigate the adequacy of the fitted models. The results can be used to compare models and to check the validity of the model being tested. Also we investigate properties under various types of model misspecification. All our works are illustrated using two sets of data measuring daily prevalence and incidence of infant diarrhoea in Salvador, Brazil. Significant clustering is identified in the data. We investigate risk factors for diarrhoea and there is strong evidence of dynamic effects being important, implying heterogeneity between individuals not explained by measured socio- economic and environmental factors.
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Siddiqi, Junaid Sagheer. "Mixture and latent class models for discrete multivariate data." Thesis, University of Exeter, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303877.
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Peluso, Alina. "Novel regression models for discrete response." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15581.
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In a regression context, the aim is to analyse a response variable of interest conditional to a set of covariates. In many applications the response variable is discrete. Examples include the event of surviving a heart attack, the number of hospitalisation days, the number of times that individuals benefit of a health service, and so on. This thesis advances the methodology and the application of regression models with discrete response. First, we present a difference-in-differences approach to model a binary response in a health policy evaluation framework. In particular, generalized linear mixed methods are employed to model multiple dependent outcomes in order to quantify the effect of an adopted pay-for-performance program while accounting for the heterogeneity of the data at the multiple nested levels. The results show how the policy had a positive effect on the hospitals' quality in terms of those outcomes that can be more influenced by a managerial activity. Next, we focus on regression models for count response variables. In a parametric framework, Poisson regression is the simplest model for count data though it is often found not adequate in real applications, particularly in the presence of excessive zeros and in the case of dispersion, i.e. when the conditional mean is different to the conditional variance. Negative Binomial regression is the standard model for over-dispersed data, but it fails in the presence of under-dispersion. Poisson-Inverse Gaussian regression can be used in the case of over-dispersed data, Generalised-Poisson regression can be employed in the case of under-dispersed data, and Conway-Maxwell Poisson regression can be employed in both cases of over- or under-dispersed data, though the interpretability of these models is ot straightforward and they are often found computationally demanding. While Jittering is the default non-parametric approach for count data, inference has to be made for each individual quantile, separate quantiles may cross and the underlying uniform random sampling can generate instability in the estimation. These features motivate the development of a novel parametric regression model for counts via a Discrete Weibull distribution. This distribution is able to adapt to different types of dispersion relative to Poisson, and it also has the advantage of having a closed form expression for the quantiles. As well as the standard regression model, generalized linear mixed models and generalized additive models are presented via this distribution. Simulated and real data applications with different type of dispersion show a good performance of Discrete Weibull-based regression models compared with existing regression approaches for count data.
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Humphreys, Keith. "Latent variable models for discrete longitudinal data with measurement error." Thesis, University of Southampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295045.
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Smith, Christopher Rand. "The Programmatic Generation of Discrete-Event Simulation Models from Production Tracking Data." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5829.
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Discrete-event simulation can be a useful tool in analyzing complex system dynamics in various industries. However, it is difficult for entry-level users of discrete-event simulation software to both collect the appropriate data to create a model and to actually generate the base-case simulation model. These difficulties decrease the usefulness of simulation software and limit its application in areas in which it could be potentially useful. This research proposes and evaluates a data collection and analysis methodology that would allow for the programmatic generation of simulation models using production tracking data. It uses data collected from a GPS device that follows products as they move through a system. The data is then analyzed by identifying accelerations in movement as the products travel and then using those accelerations to determine discrete events of the system. The data is also used to identify flow paths, pseudo-capacities, and to characterize the discrete events. Using the results of this analysis, it is possible to then generate a base-case discrete event simulation. The research finds that discrete event simulations can be programmatically generated within certain limitations. It was found that, within these limitations, the data collection and analysis method could be used to build and characterize a representative simulation model. A test scenario found that a model could be generated with 2.1% error on the average total throughput time of a product in the system, and less than 8% error on the average throughput time of a product through any particular process in the system. The research also found that the time to build a model under the proposed method is likely significantly less, as it took an experienced simulation modeler .4% of the time to build a simple model based off a real-world scenario programmatically than it did to build the model manually.
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Egger, Peter Johann. "Event history analysis : discrete-time models including unobserved heterogeneity, with applications to birth history data." Thesis, University of Southampton, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386202.
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Hyde, Eoin Ronan. "Multi-scale parameterisation of static and dynamic continuum porous perfusion models using discrete anatomical data." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:4c7df64f-b134-4b5c-8502-e34fb2c937c9.
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The aim of this thesis is to replace the intractable problem of using discrete flow models within large vascular networks with a suitably parameterised and tractable continuum perfusion model. Through this work, we directly address the hypothesis that discrete vascular data can be incorporated within continuum perfusion models via spatially-averaged parameterisation techniques. Chapter 1 reviews biological perfusion from both clinical and computational modelling perspectives, with a particular focus on myocardial perfusion. In Chapter 2, a synthetic 3D vascular network was constructed, which was controllable in terms of its size and properties. A multi-compartment static Darcy perfusion model of this discrete system was parameterised via a number of techniques. Permeabilities were derived using: (i) porosity-scaled isotropic (ϕI); (ii) Huyghe and Van Campen (HvC); and (iii) projected-PCA parameterisation methods. It was found that HvC permeabilities and pressure-coupling fields derived from the discrete data produced the best comparison to the spatially-averaged Poiseuille pressure. In Chapter 3, the construction and analysis of high-resolution anatomical arterial vascular models was undertaken. In Chapter 4, various anatomically-derived vascular networks were used to parameterise our perfusion model, including a microCT-derived rat capillary network, a single arterial subtree, and canine and porcine whole-organ arterial models. Allowing for general-connectivity (as opposed to strictly-hierarchical connectivity) yielded a significant improvement on the continuum model pressure. For the whole-organ model however, it was found that the best results were obtained by using porosity-scaled isotropic permeabilities and anatomically-derived pressure-coupling fields. It was also discovered that naturally occurring small length but relatively large radius vessels were not suitable for the HvC method. In Chapter 5, the suitability of derived parameters for use within a dynamic perfusion model was examined. It was found that the parameters derived from the original static network were adequate for application throughout the cardiac cycle. Chapter 6 presents a concluding discussion, highlighting limitations and future directions to be investigated.
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Avery, Jacob Bryan. "Data-driven modeling of the airport runway configuration selection process using maximum likelihood discrete-choice models." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103444.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 103-106).
The runway configuration is a key driver of airport capacity at any time. Several factors, such as wind speed, wind direction, visibility, traffic demand, air traffic controller workload, and the coordination of flows with neighboring airports influence the selection of the runway configuration. This paper identifies a discrete-choice model of the configuration selection process from empirical data. The model reflects the importance of various factors in terms of a utility function. Given the weather, traffic demand and the current runway configuration, the model provides a probabilistic forecast of the runway configuration at the next 15-minute interval. This prediction is then extended to obtain the probabilistic forecast of runway configuration on time horizons up to 6 hours. Case studies for Newark (EWR), John F. Kennedy (JFK), LaGuardia (LGA), and San-Francisco (SFO) airports are completed with this approach, first by assuming perfect knowledge of future weather and demand, and then using the Terminal Aerodrome Forecasts (TAFs). The results show that given the actual traffic demand and weather conditions 3 hours in advance, the models predict the correct runway configuration at EWR, JFK, LGA, and SFO with accuracies 79.5%, 63.8%, 81.3% and 82.8% respectively. Given the forecast weather and scheduled demand 3 hours in advance, the models predict the correct runway configuration at EWR, LGA, and SFO with accuracies 78.9%, 78.9% and 80.8% respectively. Finally, the discrete-choice method is applied to the entire New York Metroplex using two different methodologies and is shown to predict the Metroplex configuration with accuracies of 69.0% on a 3 hour prediction horizon.
by Jacob Bryan Avery.
S.M.
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Zheng, Xiyu. "SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4403.
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In a two-level hierarchical linear model(HLM2), the outcome as well as covariates may have missing values at any of the levels. One way to analyze all available data in the model is to estimate a multivariate normal joint distribution of variables, including the outcome, subject to missingness conditional on covariates completely observed by maximum likelihood(ML); draw multiple imputation (MI) of missing values given the estimated joint model; and analyze the hierarchical model given the MI [1,2]. The assumption is data missing at random (MAR). While this method yields efficient estimation of the hierarchical model, it often estimates the model given discrete missing data that is handled under multivariate normality. In this thesis, we evaluate how robust it is to estimate a hierarchical linear model given discrete missing data by the method. We simulate incompletely observed data from a series of hierarchical linear models given discrete covariates MAR, estimate the models by the method, and assess the sensitivity of handling discrete missing data under the multivariate normal joint distribution by computing bias, root mean squared error, standard error, and coverage probability in the estimated hierarchical linear models via a series of simulation studies. We want to achieve the following aim: Evaluate the performance of the method handling binary covariates MAR. We let the missing patterns of level-1 and -2 binary covariates depend on completely observed variables and assess how the method handles binary missing data given different values of success probabilities and missing rates.
Based on the simulation results, the missing data analysis is robust under certain parameter settings. Efficient analysis performs very well for estimation of level-1 fixed and random effects across varying success probabilities and missing rates. MAR estimation of level-2 binary covariate is not well estimated when the missing rate in level-2 binary covariate is greater than 10%.
The rest of the thesis is organized as follows: Section 1 introduces the background information including conventional methods for hierarchical missing data analysis, different missing data mechanisms, and the innovation and significance of this study. Section 2 explains the efficient missing data method. Section 3 represents the sensitivity analysis of the missing data method and explain how we carry out the simulation study using SAS, software package HLM7, and R. Section 4 illustrates the results and useful recommendations for researchers who want to use the missing data method for binary covariates MAR in HLM2. Section 5 presents an illustrative analysis National Growth of Health Study (NGHS) by the missing data method. The thesis ends with a list of useful references that will guide the future study and simulation codes we used.
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Swallow, Ben. "Bayesian multi-species modelling of non-negative continuous ecological data with a discrete mass at zero." Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/9626.
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Severe declines in the number of some songbirds over the last 40 years have caused heated debate amongst interested parties. Many factors have been suggested as possible causes for these declines, including an increase in the abundance and distribution of an avian predator, the Eurasian sparrowhawk Accipiter nisus. To test for evidence for a predator effect on the abundance of its prey, we analyse data on 10 species visiting garden bird feeding stations monitored by the British Trust for Ornithology in relation to the abundance of sparrowhawks. We apply Bayesian hierarchical models to data relating to averaged maximum weekly counts from a garden bird monitoring survey. These data are essentially continuous, bounded below by zero, but for many species show a marked spike at zero that many standard distributions would not be able to account for. We use the Tweedie distributions, which for certain areas of parameter space relate to continuous nonnegative distributions with a discrete probability mass at zero, and are hence able to deal with the shape of the empirical distributions of the data. The methods developed in this thesis begin by modelling single prey species independently with an avian predator as a covariate, using MCMC methods to explore parameter and model spaces. This model is then extended to a multiple-prey species model, testing for interactions between species as well as synchrony in their response to environmental factors and unobserved variation. Finally we use a relatively new methodological framework, namely the SPDE approach in the INLA framework, to fit a multi-species spatio-temporal model to the ecological data. The results from the analyses are consistent with the hypothesis that sparrowhawks are suppressing the numbers of some species of birds visiting garden feeding stations. Only the species most susceptible to sparrowhawk predation seem to be affected.
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Gudjonsen, Ludvik. "Combining Probabilistic and Discrete Methods for Sequence Modelling." Thesis, University of Skövde, Department of Computer Science, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-390.
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Sequence modelling is used for analysing newly sequenced proteins, giving indication of the 3-D structure and functionality. Current approaches to the modelling of protein families are either based on discrete or probabilistic methods. Here we present an approach for combining these two approaches in a hybrid model, where discrete patterns are used to model conserved regions and probabilistic models are used for variable regions. When hidden Markov models are used to model the variable regions, the hybrid method gives increased classification accuracy, compared to pure discrete or probabilistic models.
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de, Wiljes Jana [Verfasser]. "Data-Driven Discrete Spatio-Temporal Models: Problems, Methods and an Arctic Sea Ice Application / Jana de Wiljes." Berlin : Freie Universität Berlin, 2015. http://d-nb.info/1065234112/34.
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Bell, Mark. "Methods for enhancing system dynamics modelling : state-space models, data-driven structural validation & discrete-event simulation." Thesis, Lancaster University, 2015. http://eprints.lancs.ac.uk/86867/.
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System dynamics (SD) simulation models are differential equation models that often contain a complex network of relationships between variables. These models are widely used, but have a number of limitations. SD models cannot represent individual entities, or model the stochastic behaviour of these individuals. In addition, model parameters are often not observable and so values of these are based on expert opinion, rather than being derived directly from historical data. This thesis aims to address these limitations and hence enhance system dynamics modelling. This research is undertaken in the context of SD models from a major telecommunications provider. In the first part of the thesis we investigate the advantages of adding a discreteevent simulation model to an existing SD model, to form a hybrid model. There are few examples of previous attempts to build models of this type and we therefore provide an account of the approach used and its potential for larger models. Results demonstrate the advantages of the hybrid’s ability to track individuals and represent stochastic variation. In the second part of the thesis we investigate data-driven methods to validate model assumptions and estimate model parameters from historical data. This commences with use of regression based methods to assess core structural assumptions of the organisation’s SD model. This is a complex, highly nonlinear model used by the organisation for service delivery. We then attempt to estimate the parameters of this model, using a modified version of an existing approach based on state-space modelling and Kalman filtering, known as FIMLOF. One such modification, is the use of the unscented Kalman filter for nonlinear systems. After successfully estimating parameters in simulation studies, we attempt to calibrate the model for 59 geographical regions. Results demonstrate the success of our estimated parameters compared to the organisation’s default parameters in replicating historical data.
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Dimitrova, Elena Stanimirova. "Polynomial Models for Systems Biology: Data Discretization and Term Order Effect on Dynamics." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/28490.
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Systems biology aims at system-level understanding of biological systems, in particular cellular networks. The milestones of this understanding are knowledge of the structure of the system, understanding of its dynamics, effective control methods, and powerful prediction capability. The complexity of biological systems makes it inevitable to consider mathematical modeling in order to achieve these goals.
The enormous accumulation of experimental data representing the activities of the living cell has triggered an increasing interest in the reverse engineering of biological networks from data. In particular, construction of discrete models for reverse engineering of biological networks is receiving attention, with the goal of providing a coarse-grained description of such networks. In this dissertation we consider the modeling framework of polynomial dynamical systems over finite fields constructed from experimental data. We present and propose solutions to two problems inherent in this modeling method: the necessity of appropriate discretization of the data and the selection of a particular polynomial model from the set of all models that fit the data.
Data discretization, also known as binning, is a crucial issue for the construction of discrete models of biological networks. Experimental data are however usually continuous, or, at least, represented by computer floating point numbers. A major challenge in discretizing biological data, such as those collected through microarray experiments, is the typically small samples size. Many methods for discretization are not applicable due to the insufficient amount of data. The method proposed in this work is a first attempt to develop a discretization tool that takes into consideration the issues and limitations that are inherent in short data time courses. Our focus is on the two characteristics that any discretization method should possess in order to be used for dynamic modeling: preservation of dynamics and information content and inhibition of noise.
Given a set of data points, of particular importance in the construction of polynomial models for the reverse engineering of biological networks is the collection of all polynomials that vanish on this set of points, the so-called ideal of points. Polynomial ideals can be represented through a special finite generating set, known as Gröbner basis, that possesses some desirable properties. For a given ideal, however, the Gröbner basis may not be unique since its computation depends on the choice of leading terms for the multivariate polynomials in the ideal. The correspondence between data points and uniqueness of Gröbner bases is studied in this dissertation. More specifically, an algorithm is developed for finding all minimal sets of points that, added to the given set, have a corresponding ideal of points with a unique Gröbner basis. This question is of interest in itself but the main motivation for studying it was its relevance to the construction of polynomial dynamical systems.
This research has been partially supported by NIH Grant Nr. RO1GM068947-01.Ph. D.
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Will, Robert A. "The integration of seismic anisotropy and reservoir performance data for characterization of naturally fractured reservoirs using discrete feature network models." Texas A&M University, 2004. http://hdl.handle.net/1969.1/542.
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This dissertation presents the development of a method for quantitative integration of seismic (elastic) anisotropy attributes with reservoir performance data as an aid in characterization of systems of natural fractures in hydrocarbon reservoirs. This new method incorporates stochastic Discrete Feature Network (DFN) fracture modeling techniques, DFN model based fracture system hydraulic property and elastic anisotropy modeling, and non-linear inversion techniques, to achieve numerical integration of production data and seismic attributes for iterative refinement of initial trend and fracture intensity estimates. Although DFN modeling, flow simulation, and elastic anisotropy modeling are in themselves not new technologies, this dissertation represents the first known attempt to integrate advanced models for production performance and elastic anisotropy in fractured reservoirs using a rigorous mathematical inversion. The following new developments are presented:
. • Forward modeling and sensitivity analysis of the upscaled hydraulic properties of realistic DFN fracture models through use of effective permeability modeling techniques.
. • Forward modeling and sensitivity analysis of azimuthally variant seismic attributes based on the same DFN models.
. • Development of a combined production and seismic data objective function and computation of sensitivity coefficients.
. • Iterative model-based non-linear inversion of DFN fracture model trend and intensity through minimization of the combined objective function.
This new technique is demonstrated on synthetic models with single and multiple fracture sets as well as differing background (host) reservoir hydraulic and elastic properties. Results on these synthetic control models show that, given a well conditioned initial DFN model and good quality field production and seismic observations, the integration procedure results in convergence of both fracture trend and intensity in models with both single and multiple fracture sets. Tests show that for a single fracture set convergence is accelerated when the combined objective function is used as compared to a similar technique using only production data in the objective function. Tests performed on multiple fracture sets show that, without the addition of seismic anisotropy, the model fails to converge. These tests validate the importance of the new process for use in more realistic reservoir models.
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Azasoo, Makafui. "Data Science and the Ice-Cream Vendor Problem." Digital Commons @ East Tennessee State University, 2021. https://dc.etsu.edu/etd/3957.
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Newsvendor problems in Operations Research predict the optimal inventory levels necessary to meet uncertain demands. This thesis examines an extended version of a single period multi-product newsvendor problem known as the ice cream vendor problem. In the ice cream vendor problem, there are two products – ice cream and hot chocolate – which may be substituted for one another if the outside temperature is no too hot or not too cold. In particular, the ice cream vendor problem is a data-driven extension of the conventional newsvendor problem which does not require the assumption of a specific demand distribution, thus allowing the demand for ice cream and hot chocolate respectively to be temperature dependent. Using Discrete Event Simulation, we first simulate a real-world scenario of an ice cream vendor problem via a demand whose expected value is a function of temperature. A sample average approximation technique is subsequently used to transform the stochastic newsvendor program into a feature-driven linear program based on the exogenous factors of probability of rainfall and temperature. The resulting problem is a multi-product newsvendor linear program with L1-regularization. The solution to this problem yields the expected cost to the ice cream vendor as well as the optimal order quantities for ice cream and hot chocolate, respectively.
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Menezes, Gabrielito Rauter. "Ensaios sobre economia do empreendorismo." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/132965.
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Esta tese é composta por três ensaios sobre Economia do Empreendedorismo. O primeiro deles trata sobre os determinantes do empreendedorismo no Brasil a partir de modelos de escolha ocupacional, usando os microdados da Pesquisa Nacional por Amostra por Domicílios (PNAD) do ano de 2012. A estratégia empírica adotada empregou os modelos de escolha discreta na estimação da escolha ocupacional. Os resultados demonstraram que existem efeitos significativos para as variáveis: anos de estudos iniciais, sexo, estado civil assim como pensionista e aposentado. Para completar a análise foram estimadas as equações de rendimento, as quais explicam a escolha pela ocupação empreendedora em função dos ganhos relativos ao trabalho assalariado. Já o segundo ensaio tem como objetivo apresentar uma evidência empírica para a relação existente entre empreendedorismo e corrupção nos estados brasileiros, utilizando uma abordagem teórica e empírica. Este artigo utiliza um indicador objetivo de corrupção governamental estadual baseado no Cadastro de Contas Irregulares do Tribunal de Contas da União (CADIRREG) como proxy para a corrupção regional e a abertura de novas empresas per capita como medida para a atividade empreendedora. Foram utilizados o método de dados de painel estático, dinâmico e o método GMMSYS, estes últimos empregados para corrigir possíveis problemas de endogeneidade. Os resultados encontrados mostraram-se coerentes com a hipótese teórica “grease in the wheels”, na qual a corrupção influencia positivamente a atividade empreendedora em países em desenvolvimento com elevada burocracia. Por fim, o terceiro ensaio avalia os impactos do empreendedorismo via inovação a partir do Global Trade Analysis Project – GTAP, um modelo de equilíbrio geral computável (EGC), destacando os impactos no crescimento econômicoe no bem-estar geral da economia. Os resultados mostraram-se coerentes com a literatura da Economia do Empreendedorismo, mostrando que aumento no empreendedorismo conduz a uma elevação no crescimento econômico e bem-estar.This thesis consists of three essays on the Economics Entrepreneurship. The first deals with the determinants of entrepreneurship in Brazil from occupational choice models, using the data from the National Household Sample Survey (PNAD) of 2012. The empirical strategy adopted has employed discrete choice models in the estimation of occupational choice. The results showed that there are significant effects on the variables: years of initial studies, gender, marital status as well as pensioners and retired. To complete the analysis were estimated earnings equations, which explains the choice by the entrepreneurial occupation in terms of earnings for paid employment. The second test aims to present empirical evidence for the relationship between entrepreneurship and corruption in the Brazilian states, using a theoretical and empirical approach. This article uses an objective indicator of state government corruption based on the Register of Irregular accounts of the Court of Audit (CADIRREG) as a proxy for regional corruption and the opening of new companies per capita as a measure for regional entrepreneurial activity. They used the method of static panel data, dynamic and GMM-SYS method to correct the endogeneity problem. The results proved to be consistent with the theoretical hypothesis "grease in the wheels" in which corruption positively influence the entrepreneurial activity in developing countries with high bureaucracy. Finally, the third test evaluates the impacts of entrepreneurship via innovation from the Global Trade Analysis Project - GTAP, a model of computable general equilibrium (CGE), highlighting the impacts on economic growth and overall well-being economy. The results were consistent with the literatureof Entrepreneurship Economics, showing that increased entrepreneurship leads to a rise in economic growth and well-being.
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Babajide, Adedoyin. "Conflict and economic growth in Sub-Saharan Africa." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/36256.
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This thesis investigates the relationship between conflict, economic growth, state capacity and natural resources in Sub-Saharan Africa. It contributes to the limited research in this area and empirically examines these relationships using different econometric models. The first empirical chapter uses a panel dataset that covers the period 1997 - 2013 to analyse the effects of economic growth on conflict in Nigeria using the negative binomial model. The findings support the direct relationship between economic growth and conflict in Nigeria. Controlling for other factors, the results indicate that increase in growth rate - measured by annual growth rate of GDP per capita - decreases the expected number of conflicts. The study finds no evidence of a relationship between levels of wealth in a state and the incidence of conflicts. The analysis controls for factors such as spill-over effects from other states and year and state effects. Finally, to address potential concerns that economic growth could be a cause of conflict or that other unobserved factors could confound the relationship between economic growth and conflict, the chapter employs instrumental variable (IV) estimation using percentage change in rainfall as an instrument. The results with the IV estimation are similar to the results without IV in terms of both sign and significance, indicating that the negative effect of economic growth on conflicts is not due to reverse causality or omitted variables. For robustness checks, a Panel Autoregressive model (PVAR) is also employed. The second empirical chapter analyses the effect of conflict on state capacity in Sub-Saharan Africa. State capacity is measured in terms of fiscal and legal capacity. It also looks at the effects of internal and external conflicts on state capacity. The chapter adopts the Ordinary least squared (OLS) and the system generalised methods of moments (GMM) estimation methods to analyse the panel data consisting of 49 Sub-Saharan countries over the period 2000 - 2015. The results suggest that conflicts have a negative and significant effect on state capacity. However, when military expenditure is used as a proxy for state capacity it is found that conflict strengthens state capacity. The results are consistent with theoretical argument that internal conflicts polarise societies and make it more difficult for governments to reach a consensus in investing in state capacity, while external conflicts mobilise domestic population against a common enemy thereby helping in state capacity building. Finally, the third empirical chapter examines the effect of natural resources on conflict onset and duration using discrete choice models with a dataset covering the period 1980 -2016. The results on the duration analysis show that natural resources prolong duration of conflicts. However, it is found that not all natural resources prolong duration of conflicts. Oil production does not seem to affect duration, whereas oil reserves and gas production lengthens the duration. The findings from the onset analysis show that both production and reserves of natural resources increase the risk of conflict onset.
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Sotiropoulos, Pesiridis Konstantinos. "Parallel Simulation of SystemC Loosely-Timed Transaction Level Models." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-227806.
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Parallelizing the development cycles of hardware and software is becoming the industry’s norm for reducing time to market for electronic devices. In the absence of hardware, software development is based on a virtual platform; a fully functional software model of a system under development, able to execute unmodified code. A Transaction Level Model, expressed with the SystemC TLM 2.0 language, is one of the many possible ways for constructing a virtual platform. Under SystemC’s simulation engine, hardware and software is being co-simulated. However, the sequential nature of the reference implementation of the SystemC’s simulation kernel, is a limiting factor. Poor simulation performance often constrains the scope and depth of the design decisions that can be evaluated. It is the main objective of this thesis’ project to demonstrate the feasibility of parallelizing the co-simulation of hardware and software using Transaction Level Models, outside SystemC’s reference simulation environment. The major obstacle identified is the preservation of causal relations between simulation events. The solution is obtained by using the process synchronization mechanism known as the Chandy/Misra/Bryantt algorithm. To demonstrate our approach and evaluate under which conditions a speedup can be achieved, we use the model of a cache-coherent, symmetric multiprocessor executing a synthetic application. Two versions of the model are used for the comparison; the parallel version, based on the Message Passing Interface 3.0, which incorporates the synchronization algorithm and an equivalent sequential model based on SystemC TLM 2.0. Our results indicate that by adjusting the parameters of the synthetic application, a certain threshold is reached, above which a significant speedup against the sequential SystemC simulation is observed. Although performed manually, the transformation of a SystemC TLM 2.0 model into a parallel MPI application is deemed feasible.
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Comas, Cufí Marc. "Aportacions de l'anàlisi composicional a les mixtures de distribucions." Doctoral thesis, Universitat de Girona, 2018. http://hdl.handle.net/10803/664902.
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The present thesis is a compendium of three original works produced between
2014 and 2018. The papers have a common link: they are different
contributions made by compositional data analysis to the study of the models
based on mixtures of probability distributions. In brief, we could say that
compositional data analysis is a methodology that consists of studying a
sample of measures that are strictly positive from a relative point of view.
Mixtures of distributions are a specific type of probability distribution
defined to be the convex linear combination of other distributionsLa present tesi representa un compendi de tres treballs originals realitzats durant els anys 2014-2018. Aquests treballs comparteixen un nexe comú: tots ells són diferents aportacions de l'anàlisi composicional a l'estudi dels models basats en mixtures de distribucions de probabilitat. D'una forma molt breu, podríem dir que l'anàlisi composicional és una metodologia consistent en estudiar una mostra de mesures estrictament positives des d'un punt de vista relatiu. Les mixtures de distribucions, també anomenades barreges de distribucions, són un tipus particular de distribucions de probabilitat definides com la combinació lineal convexa d'altres distribucions
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Reichmanová, Barbora. "Užití modelů diskrétních dat." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-392846.
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Při analýze dat růstu rostlin v řádku dané délky bychom měli uvažovat jak pravděpodobnost, že semínko zdárně vyroste, tak i náhodný počet semínek, které byly zasety. Proto se v celé práci věnujeme analýze náhodných sum, kde počet nezávisle stejně rozdělených sčítanců je na nich nezávislé náhodné číslo. První část práce věnuje pozornost teoretickému základu, definuje pojem náhodná suma a uvádí vlastnosti, jako jsou číslené míry polohy nebo funkční charakteristiky popisující dané rozdělení. Následně je diskutována metoda odhadu parametrů pomocí maximální věrohodnosti a zobecněné lineární modely. Metoda kvazi-věrohodnosti je též krátce zmíněna. Tato část je ilustrována příklady souvisejícími s výchozím problémem. Poslední kapitola se věnuje aplikaci na reálných datech a následné analýze.
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Roddam, Andrew Wilfred. "Some problems in the theory & application of graphical models." Thesis, University of Oxford, 1999. http://ora.ox.ac.uk/objects/uuid:b90d5dbc-6e9a-4c5e-bdca-0c3558b4ee17.
Full textAbstract:
A graphical model is simply a representation of the results of an analysis of relationships between sets of variables. It can include the study of the dependence of one variable, or a set of variables on another variable or sets of variables, and can be extended to include variables which could be considered as intermediate to the others. This leads to the concept of representing these chains of relationships by means of a graph; where variables are represented by vertices, and relationships between the variables are represented by edges. These edges can be either directed or undirected, depending upon the type of relationship being represented. The thesis investigates a number of outstanding problems in the area of statistical modelling, with particular emphasis on representing the results in terms of a graph. The thesis will study models for multivariate discrete data and in the case of binary responses, some theoretical results are given on the relationship between two common models. In the more general setting of multivariate discrete responses, a general class of models is studied and an approximation to the maximum likelihood estimates in these models is proposed. This thesis also addresses the problem of measurement errors. An investigation into the effect that measurement error has on sample size calculations is given with respect to a general measurement error specification in both linear and binary regression models. Finally, the thesis presents, in terms of a graphical model, a re-analysis of a set of childhood growth data, collected in South Wales during the 1970s. Within this analysis, a new technique is proposed that allows the calculation of derived variables under the assumption that the joint relationships between the variables are constant at each of the time points.
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Patel, Hiren Dhanji. "HEMLOCK: HEterogeneous ModeL Of Computation Kernel for SystemC." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/9632.
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As SystemC gains popularity as a System Level Design Language (SLDL) for System-On-Chip (SOC) designs, heterogeneous modelling and efficient simulation become increasingly important. The key in making an SLDL heterogeneous is the facility to express different Models Of Computation (MOC). Currently, all SystemC models employ a Discrete-Event simulation kernel making it difficult to express most MOCs without specific designer guidelines. This often makes it unnatural to express different MOCs in SystemC. For the simulation framework, this sometimes results in unnecessary delta cycles for models away from the Discrete-Event MOC, hindering the simulation performance of the model. Our goal is to extend SystemC's simulation framework to allow for better modelling expressiveness and efficiency for the Synchronous Data Flow (SDF) MOC. The SDF MOC follows a paradigm where the production and consumption rates of data by a function block are known a priori. These systems are common in Digital Signal Processing applications where relative sample rates are specified for every component. Knowledge of these rates enables the use of static scheduling. When compared to dynamic scheduling of SDF models, we experience a noticeable improvement in simulation efficiency. We implement an extension to the SystemC kernel that exploits such static scheduling for SDF models and propose designer style guidelines for modelers to use this extension. The modelling paradigm becomes more natural to SDF which results to better simulation efficiency. We will distribute our implementation to the SystemC community to demonstrate that SystemC can be a heterogeneous SLDL.Master of Science
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Khan, Khalid. "The Evaluation of Well-known Effort Estimation Models based on Predictive Accuracy Indicators." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4778.
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Accurate and reliable effort estimation is still one of the most challenging processes in software engineering. There have been numbers of attempts to develop cost estimation models. However, the evaluation of model accuracy and reliability of those models have gained interest in the last decade. A model can be finely tuned according to specific data, but the issue remains there is the selection of the most appropriate model. A model predictive accuracy is determined by the difference of the various accuracy measures. The one with minimum relative error is considered to be the best fit. The model predictive accuracy is needed to be statistically significant in order to be the best fit. This practice evolved into model evaluation. Models predictive accuracy indicators need to be statistically tested before taking a decision to use a model for estimation. The aim of this thesis is to statistically evaluate well known effort estimation models according to their predictive accuracy indicators using two new approaches; bootstrap confidence intervals and permutation tests. In this thesis, the significance of the difference between various accuracy indicators were empirically tested on the projects obtained from the International Software Benchmarking Standard Group (ISBSG) data set. We selected projects of Un-Adjusted Function Points (UFP) of quality A. Then, the techniques; Analysis Of Variance ANOVA and regression to form Least Square (LS) set and Estimation by Analogy (EbA) set were used. Step wise ANOVA was used to form parametric model. K-NN algorithm was employed in order to obtain analogue projects for effort estimation use in EbA. It was found that the estimation reliability increased with the pre-processing of the data statistically, moreover the significance of the accuracy indicators were not only tested statistically but also with the help of more complex inferential statistical methods. The decision of selecting non-parametric methodology (EbA) for generating project estimates in not by chance but statistically proved.
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Paule, Inès. "Adaptation of dosing regimen of chemotherapies based on pharmacodynamic models." Phd thesis, Université Claude Bernard - Lyon I, 2011. http://tel.archives-ouvertes.fr/tel-00846454.
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There is high variability in response to cancer chemotherapies among patients. Its sources are diverse: genetic, physiologic, comorbidities, concomitant medications, environment, compliance, etc. As the therapeutic window of anticancer drugs is usually narrow, such variability may have serious consequences: severe (even life-threatening) toxicities or lack of therapeutic effect. Therefore, various approaches to individually tailor treatments and dosing regimens have been developed: a priori (based on genetic information, body size, drug elimination functions, etc.) and a posteriori (that is using information of measurements of drug exposure and/or effects). Mixed-effects modelling of pharmacokinetics and pharmacodynamics (PK-PD), combined with Bayesian maximum a posteriori probability estimation of individual effects, is the method of choice for a posteriori adjustments of dosing regimens. In this thesis, a novel approach to adjust the doses on the basis of predictions, given by a model for ordered categorical observations of toxicity, was developed and investigated by computer simulations. More technical aspects concerning the estimation of individual parameters were analysed to determine the factors of good performance of the method. These works were based on the example of capecitabine-induced hand-and-foot syndrome in the treatment of colorectal cancer. Moreover, a review of pharmacodynamic models for discrete data (categorical, count, time-to-event) was performed. Finally, PK-PD analyses of hydroxyurea in the treatment of sickle cell anemia were performed and used to compare different dosing regimens and determine the optimal measures for monitoring the treatment
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Liu, Xiaodong. "Econometrics on interactions-based models methods and applications /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180283230.
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García, Gómez Pilar. "Health, informal care and labour market outcomes in Europe." Doctoral thesis, Universitat Pompeu Fabra, 2008. http://hdl.handle.net/10803/7376.
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Esta tesis contribuye a la literatura analizando los efectos causales que el estado de salud tiene sobre la participación laboral en la población en edad de trabajar. De este modo, analiza los efectos que un deterioro en el estado de salud tiene sobre la participación laboral del individuo, así como los efectos de proveer cuidados informales sobre la participación laboral femenina. El primer capítulo utiliza una aproximación empírica homogénea en nueve países europeos, lo que hace posible relacionar las diferencias encontradas con diferencias en el contexto institucional. El segundo capítulo analiza el papel que juega el estado de salud en las transiciones hacia y fuera del empleo. Los resultados muestran que el estado de salud general afecta simétricamente las entradas y salidas del empleo, mientras que cambios en el estado de salud mental sólo influyen el riesgo de abandonar el empleo. El tercer capítulo examina los efectos de varios tipos de cuidados informales en el comportamiento laboral femenino. Los resultados sugieren que los costes de oportunidad laborales aparecen en aquellas mujeres que conviven con la persona dependiente, al mismo tiempo que los efectos negativos surgen cuando se proveen cuidados informales por un período superior al año.This thesis aims to contribute to the literature with an attempt to identify the causal effects of health on labour market outcomes in the working-age population. I analyse the effects of the onset of a health shock on the individuals' labour market outcomes, and also the effects of caregiving on female labour participation. The first chapter uses a homogeneous empirical framework to estimate the first set of effects on nine European countries, which allows me to relate the empirical estimates to differences in social security arrangements across these countries. The second chapter analyses the role of health in exits out of and entries into employment and the results show that general health affects symmetrically entries into and exits out of employment, but changes in mental health status influence only the hazard of non-employment for the stock sample of workers. The third chapter examines the effects of various types of informal care on female labour behaviour and the results suggest the existence of labour opportunity costs for those women who live with the dependent person they care for, and the negative effects appear when caregiving for more than a year.
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Kalktawi, Hadeel Saleh. "Discrete Weibull regression model for count data." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/14476.
Full textAbstract:
Data can be collected in the form of counts in many situations. In other words, the number of deaths from an accident, the number of days until a machine stops working or the number of annual visitors to a city may all be considered as interesting variables for study. This study is motivated by two facts; first, the vital role of the continuous Weibull distribution in survival analyses and failure time studies. Hence, the discrete Weibull (DW) is introduced analogously to the continuous Weibull distribution, (see, Nakagawa and Osaki (1975) and Kulasekera (1994)). Second, researchers usually focus on modeling count data, which take only non-negative integer values as a function of other variables. Therefore, the DW, introduced by Nakagawa and Osaki (1975), is considered to investigate the relationship between count data and a set of covariates. Particularly, this DW is generalised by allowing one of its parameters to be a function of covariates. Although the Poisson regression can be considered as the most common model for count data, it is constrained by its equi-dispersion (the assumption of equal mean and variance). Thus, the negative binomial (NB) regression has become the most widely used method for count data regression. However, even though the NB can be suitable for the over-dispersion cases, it cannot be considered as the best choice for modeling the under-dispersed data. Hence, it is required to have some models that deal with the problem of under-dispersion, such as the generalized Poisson regression model (Efron (1986) and Famoye (1993)) and COM-Poisson regression (Sellers and Shmueli (2010) and Sáez-Castillo and Conde-Sánchez (2013)). Generally, all of these models can be considered as modifications and developments of Poisson models. However, this thesis develops a model based on a simple distribution with no modification. Thus, if the data are not following the dispersion system of Poisson or NB, the true structure generating this data should be detected. Applying a model that has the ability to handle different dispersions would be of great interest. Thus, in this study, the DW regression model is introduced. Besides the exibility of the DW to model under- and over-dispersion, it is a good model for inhomogeneous and highly skewed data, such as those with excessive zero counts, which are more disperse than Poisson. Although these data can be fitted well using some developed models, namely, the zero-inated and hurdle models, the DW demonstrates a good fit and has less complexity than these modifed models. However, there could be some cases when a special model that separates the probability of zeros from that of the other positive counts must be applied. Then, to cope with the problem of too many observed zeros, two modifications of the DW regression are developed, namely, zero-inated discrete Weibull (ZIDW) and hurdle discrete Weibull (HDW) models. Furthermore, this thesis considers another type of data, where the response count variable is censored from the right, which is observed in many experiments. Applying the standard models for these types of data without considering the censoring may yield misleading results. Thus, the censored discrete Weibull (CDW) model is employed for this case. On the other hand, this thesis introduces the median discrete Weibull (MDW) regression model for investigating the effect of covariates on the count response through the median which are more appropriate for the skewed nature of count data. In other words, the likelihood of the DW model is re-parameterized to explain the effect of the predictors directly on the median. Thus, in comparison with the generalized linear models (GLMs), MDW and GLMs both investigate the relations to a set of covariates via certain location measurements; however, GLMs consider the means, which is not the best way to represent skewed data. These DW regression models are investigated through simulation studies to illustrate their performance. In addition, they are applied to some real data sets and compared with the related count models, mainly Poisson and NB models. Overall, the DW models provide a good fit to the count data as an alternative to the NB models in the over-dispersion case and are much better fitting than the Poisson models. Additionally, contrary to the NB model, the DW can be applied for the under-dispersion case.
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Wu, Hongqian. "Proportional likelihood ratio mixed model for longitudinal discrete data." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2296.
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A semiparametric proportional likelihood ratio model was proposed by Luo and Tsai (2012) which is suitable for modeling a nonlinear monotonic relationship between the response variable and a covariate. Extending the generalized linear model, this model leaves the probability distribution unspecified but estimates it from the data. In this thesis, we propose to extend this model into analyzing the longitudinal data by incorporating random effects into the linear predictor. By using this model as the conditional density of the response variable given the random effects, we present a maximum likelihood approach for model estimation and inference. Two numerical estimation procedures were developed for response variables with finite support, one based on the Newton-Raphson algorithm and the other one based on generalized expectation maximization (GEM) algorithm. In both estimation procedures, Gauss-Hermite quadrature is employed to approximate the integrals.
Upon convergence, the observed information matrix is estimated through the second-order numerical differentiation of the log likelihood function. Asymptotic properties of the maximum likelihood estimator are established under certain regularity conditions and simulation studies are conducted to assess its finite sample properties and compare the proposed model to the generalized linear mixed model. The proposed method is illustrated in an analysis of data from a multi-site observational study of prodromal Huntington's disease.
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Khan, Mohammad. "Variational learning for latent Gaussian model of discrete data." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43640.
Full textAbstract:
This thesis focuses on the variational learning of latent Gaussian models for discrete data.
The learning is difficult since the discrete-data likelihood is not conjugate to the Gaussian prior.
Existing methods to solve this problem are either inaccurate or slow.
We consider a variational approach based on evidence lower bound optimization.
We solve the following two main problems of the variational approach: the computational inefficiency associated with the maximization of the lower bound and the intractability of the lower bound.
For the first problem, we establish concavity of the lower bound and design fast learning algorithms using concave optimization.
For the second problem, we design tractable and accurate lower bounds, some of which have provable error guarantees.
We show that these lower bounds not only make accurate variational learning possible, but can also give rise to algorithms with a wide variety of speed-accuracy trade-offs.
We compare various lower bounds, both theoretically and experimentally, giving clear design guidelines for variational algorithms.
Through application to real-world data, we show that the variational approach can be more accurate and faster than existing methods.
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Shen, Yu. "Car fleet modelling : Data processing and discrete choice model estimation." Thesis, KTH, Transportvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-43719.
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This thesis deals with the modelling of the choice of new car based on the registration data of the whole Sweden car fleet for 2005 to 2010. It is divided into two parts. In the first part, to obtain the observations of new car choices for the discrete choice modelling, a subset based on the first registration date of each car is extracted. Then, a descriptive analysis based on the new car choice data is presented to find the variances of the attributes for the modelling. Specifically, two major issues are paid attention to. One is the change of market share of each car make in these years and the other is the incremental demand of diesel and hybrid fuel cars. The second part of the thesis deals with the discrete choice modelling. In order to designate the alternatives, another dataset showing the new car supply in Sweden is introduced. In the supply data, the alternatives are shown in the car version level, whereas the registration data only contain the names of car models. Additionally, the supply data also have some attributes that are unavailable in the registration, e.g. price. Thus, this thesis presents various matching methods to match the supply and the registration to define the alternatives for the modelling and also to obtain a higher precision of each attribute than that in matching with model names only. Finally, we choose to match the data by the same model name with the same maximum power, which is defined as the “model-engine” level. Therefore, based on these model-engine level alternatives, 18 MNL models are estimated from 2005 to 2010, with 3 different ownerships, namely private owned, company owned and company owned but leasing to its employee which is named as “leasing users”. The results show the slump of the brand constants of Saab among these years in private owners and leasing users due to the close-down crisis when the coefficient of Volvo is fixed to zero. By contrast, the brand value of Kia for private owners and the value of VW for leasing users go up. Meanwhile, this thesis analyses a shift of car buyers’ attitude to the alternative fuel car from negative in 2006 to positive in 2007 when a “clean car” compensation policy is implemented from Jan. 2007 to Jul. 2009. And in 2010, the coefficient of the alternative fuel remains positive. These results indicate that this policy was quite successful.
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Dong, Fanglong. "Bayesian Model Checking in Multivariate Discrete Regression Problems." Bowling Green State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1223329230.
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Wu, Hao. "Probabilistic Modeling of Multi-relational and Multivariate Discrete Data." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/74959.
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Modeling and discovering knowledge from multi-relational and multivariate
discrete data is a crucial task that arises in many research and application
domains, e.g. text mining, intelligence analysis, epidemiology, social science,
etc. In this dissertation, we study and address three problems involving the
modeling of multi-relational discrete data and multivariate multi-response count
data, viz. (1) discovering surprising patterns from multi-relational data, (2)
constructing a generative model for multivariate categorical data, and (3)
simultaneously modeling multivariate multi-response count data and estimating
covariance structures between multiple responses.
To discover surprising multi-relational patterns, we first study the ``where
do I start?'' problem originating from intelligence analysis. By studying nine
methods with origins in association analysis, graph metrics, and probabilistic
modeling, we identify several classes of algorithmic strategies that can supply
starting points to analysts, and thus help to discover interesting
multi-relational patterns from datasets. To actually mine for interesting
multi-relational patterns, we represent the multi-relational patterns as dense
and well-connected chains of biclusters over multiple relations, and model the
discrete data by the maximum entropy principle, such that in a statistically
well-founded way we can gauge the surprisingness of a discovered bicluster chain
with respect to what we already know. We design an algorithm for approximating
the most informative multi-relational patterns, and provide strategies to
incrementally organize discovered patterns into the background model. We
illustrate how our method is adept at discovering the hidden plot in multiple
synthetic and real-world intelligence analysis datasets. Our approach naturally
generalizes traditional attribute-based maximum entropy models for single
relations, and further supports iterative, human-in-the-loop, knowledge
discovery.
To build a generative model for multivariate categorical data, we apply the
maximum entropy principle to propose a categorical maximum entropy model such
that in a statistically well-founded way we can optimally use given prior
information about the data, and are unbiased otherwise. Generally, inferring the
maximum entropy model could be infeasible in practice. Here, we leverage the
structure of the categorical data space to design an efficient model inference
algorithm to estimate the categorical maximum entropy model, and we demonstrate
how the proposed model is adept at estimating underlying data distributions. We
evaluate this approach against both simulated data and US census datasets, and
demonstrate its feasibility using an epidemic simulation application.
Modeling data with multivariate count responses is a challenging problem due to
the discrete nature of the responses. Existing methods for univariate count
responses cannot be easily extended to the multivariate case since the
dependency among multiple responses needs to be properly accounted for. To model
multivariate data with multiple count responses, we propose a novel multivariate
Poisson log-normal model (MVPLN). By simultaneously estimating the regression
coefficients and inverse covariance matrix over the latent variables with an
efficient Monte Carlo EM algorithm, the proposed model takes advantages of
association among multiple count responses to improve the model prediction
accuracy. Simulation studies and applications to real world data are conducted
to systematically evaluate the performance of the proposed method in comparison
with conventional methods.Ph. D.
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Batista, Douglas Toledo. "Modelos para dados de contagem com superdispersão: uma aplicação em um experimento agronômico." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-21092015-105550/.
Full textAbstract:
O modelo de referência para dados de contagem é o modelo de Poisson. A principal característica do modelo de Poisson é a pressuposição de que a média e a variância são iguais. No entanto, essa relação de média-variância nem sempre ocorre em dados observacionais. Muitas vezes, a variância observada nos dados é maior do que a variância esperada, fenômeno este conhecido como superdispersão. O objetivo deste trabalho constitui-se na aplicação de modelos lineares generalizados, a fim de selecionar um modelo adequado para acomodar de forma satisfatória a superdispersão presente em dados de contagem. Os dados provêm de um experimento que objetivava avaliar e caracterizar os parâmetros envolvidos no florescimento de plantas adultas da laranjeira variedade \"x11\", enxertadas nos limoeiros das variedades \"Cravo\" e \"Swingle\". Primeiramente ajustou-se o modelo de Poisson com função de ligação canônica. Por meio da deviance, estatística X2 de Pearson e do gráfico half-normal plot observou-se forte evidência de superdispersão. Utilizou-se, então, como modelos alternativos ao Poisson, os modelos Binomial Negativo e Quase-Poisson. Verificou que o modelo Quase-Poisson foi o que melhor se ajustou aos dados, permitindo fazer inferências mais precisas e interpretações práticas para os parâmetros do modelo.The reference model for count data is the Poisson model. The main feature of Poisson model is the assumption that mean and variance are equal. However, this mean-variance relationship rarely occurs in observational data. Often, the observed variance is greater than the expected variance, a phenomenon known as overdispersion. The aim of this work is the application of generalized linear models, in order to select an appropriated model to satisfactorily accommodate the overdispersion present in the data. The data come from an experiment that aimed to evaluate and characterize the parameters involved in the flowering of orange adult plants of the variety \"x11\" grafted on \"Cravo\" and \"Swingle\". First, the data were submitted to adjust by Poisson model with canonical link function. Using deviance, generalized Pearson chi-squared statistic and half-normal plots, it was possible to notice strong evidence of overdispersion. Thus, alternative models to Poisson were used such as the negative binomial and Quasi-Poisson models. The Quasi-Poisson model presented the best fit to the data, allowing more accurate inferences and practices interpretations for the parameters.
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Rizzato, Fernanda Bührer. "Modelos para análise de dados discretos longitudinais com superdispersão." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-23032012-092433/.
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Dados longitudinais na forma de contagens e na forma binária são muito comuns, os quais, frequentemente, podem ser analisados por distribuições de Poisson e de Bernoulli, respectivamente, pertencentes à família exponencial. Duas das principais limitações para modelar esse tipo de dados são: (1) a ocorrência de superdispersão, ou seja, quando a variabilidade dos dados não é adequadamente descrita pelos modelos, que muitas vezes apresentam uma relação pré-estabelecida entre a média e a variância, e (2) a correlação existente entre medidas realizadas repetidas vezes na mesma unidade experimental. Uma forma de acomodar a superdispersão é pela utilização das distribuições binomial negativa e beta binomial, ou seja, pela inclusão de um efeito aleatório com distribuição gama quando se considera dados provenientes de contagens e um efeito aleatório com distribuição beta quando se considera dados binários, ambos introduzidos de forma multiplicativa. Para acomodar a correlação entre as medidas realizadas no mesmo indivíduo podem-se incluir efeitos aleat órios com distribuição normal no preditor linear. Esses situações podem ocorrer separada ou simultaneamente. Molenberghs et al. (2010) propuseram modelos que generalizam os modelos lineares generalizados mistos Poisson-normal e Bernoulli-normal, incorporando aos mesmos a superdispersão. Esses modelos foram formulados e ajustados aos dados, usando-se o método da máxima verossimilhança. Entretanto, para um modelo de efeitos aleatórios, é natural pensar em uma abordagem Bayesiana. Neste trabalho, são apresentados modelos Bayesianos hierárquicos para dados longitudinais, na forma de contagens e binários que apresentam superdispersão. A análise Bayesiana hierárquica é baseada no método de Monte Carlo com Cadeias de Markov (MCMC) e para implementação computacional utilizou-se o software WinBUGS. A metodologia para dados na forma de contagens é usada para a análise de dados de um ensaio clínico em pacientes epilépticos e a metodologia para dados binários é usada para a análise de dados de um ensaio clínico para tratamento de dermatite.Longitudinal count and binary data are very common, which often can be analyzed by Poisson and Bernoulli distributions, respectively, members of the exponential family. Two of the main limitations to model this data are: (1) the occurrence of overdispersion, i.e., the phenomenon whereby variability in the data is not adequately captured by the model, and (2) the accommodation of data hierarchies owing to, for example, repeatedly measuring the outcome on the same subject. One way of accommodating overdispersion is by using the negative-binomial and beta-binomial distributions, in other words, by the inclusion of a random, gamma-distributed eect when considering count data and a random, beta-distributed eect when considering binary data, both introduced by multiplication. To accommodate the correlation between measurements made in the same individual one can include normal random eects in the linear predictor. These situations can occur separately or simultaneously. Molenberghs et al. (2010) proposed models that simultaneously generalizes the generalized linear mixed models Poisson-normal and Bernoulli-normal, incorporating the overdispersion. These models were formulated and tted to the data using maximum likelihood estimation. However, these models lend themselves naturally to a Bayesian approach as well. In this paper, we present Bayesian hierarchical models for longitudinal count and binary data in the presence of overdispersion. A hierarchical Bayesian analysis is based in the Monte Carlo Markov Chain methods (MCMC) and the software WinBUGS is used for the computational implementation. The methodology for count data is used to analyse a dataset from a clinical trial in epileptic patients and the methodology for binary data is used to analyse a dataset from a clinical trial in toenail infection named onychomycosis.
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Wang, Yan. "An integrative process mining approach to mine discrete event simulation model from event data." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0183/document.
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L'inférence d’un système, par la reconstruction de la structure à partir de l’analyse de son comportement, est reconnue comme un problème critique. Dans la théorie des systèmes, la structure et le comportement se situent aux extrémités de la hiérarchie qui définit la connaissance du système. L'inférence d’un système peut être également considérée comme l’escalade de la hiérarchie depuis la connaissance de bas niveau vers la connaissance de plus haut niveau. Ceci n'est possible que sous des conditions maitrisées et justifiées. Dans cette thèse, une nouvelle méthode d'inférence de système est proposée. La méthode proposée étend la technique Process Mining pour extraire des connaissances depuis les données des événements du système. Les aspects de modularité, de fréquence et de synchronisation peuvent être extraits des données. Ils sont intégrés ensemble pour construire un modèle Fuzzy-Discrete Event System Specification (Fuzzy-DEVS). La méthode proposée, également appelée méthode D2FD (Data to Fuzzy-DEVS), comprend trois étapes: (1) l’extraction depuis des journaux d’évènements (registres) obtenus à partir des données générées par le système en utilisant une approche conceptuelle; (2) la découverte d'un système de transition, en utilisant des techniques de découverte de processus; (3) l'intégration de méthodes Fuzzy pour générer automatiquement un modèle Fuzzy-DEVS à partir du système de transition. La dernière étape est de l’implémenter cette contribution en tant que plugin dans l'environnement Process Mining Framework (ProM). Afin de valider les modèles construits, une approximation de modèle basée sur le morphisme et une méthode prédictive intégrée à Granger Causality sont proposées. Deux études de cas sont présentées dans lesquelles le modèle Fuzzy-DEVS est déduit à partir de données réelles, où l'outil SimStudio est utilisé pour sa simulation. Les modèles ainsi construits et les résultats de simulation sont validés par comparaison à d'autres modèlesSystem inference, i.e., the building of system structure from system behavior, is widely recognized as a critical challenging issue. In System Theory, structure and behavior are at the extreme sides of the hierarchy that defines knowledge about the system. System inference is known as climbing the hierarchy from less to more knowledge. In addition, it is possible only under justifying conditions. In this thesis, a new system inference method is proposed. The proposed method extends the process mining technique to extract knowledge from event data and to represent complex systems. The modularity, frequency and timing aspects can be extracted from the data. They are integrated together to construct the Fuzzy Discrete Event System Specification (Fuzzy-DEVS) model. The proposed method is also called D2FD (Data to Fuzzy-DEVS) method, and consists of three stages: (1) extraction of event logs from event data by using the conceptual structure; (2) discovery of a transition system, using process discovery techniques; (3) integration of fuzzy methods to automatically generate a Fuzzy-DEVS model from the transition system. The last stage is implemented as a plugin in the Process Mining Framework (ProM) environment. In order to validate constructed models, morphism-based model approximation and predictive method integrated with Granger Causality are proposed. Two case studies are presented in which Fuzzy-DEVS model is inferred from real life data, and the SimStudio tool is used for its simulation. The constructed models and simulation results are validated by comparing to other models
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Bahr, Hubert. "DATA BANDWIDTH REDUCTION TECHNIQUES FOR DISTRIBUTED EMBEDDED SIMULATIO." Doctoral diss., University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2778.
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Maintaining coherence between the independent views of multiple participants at distributed locations is essential in an Embedded Simulation environment. Currently, the Distributed Interactive Simulation (DIS) protocol maintains coherence by broadcasting the entity state streams from each simulation station. In this dissertation, a novel alternative to DIS that replaces the transmitting sources with local sources is developed, validated, and assessed by analytical and experimental means. The proposed Concurrent Model approach reduces the communication burden to transmission of only synchronization and model-update messages. Necessary and sufficient conditions for the correctness of Concurrent Models in a discrete event simulation environment are established by developing Behavioral Congruence ¨B(EL, ER) and Temporal Congruence ¨T(t, ER) functions. They indicate model discrepancies with respect to the simulation time t, and the local and remote entity state streams EL and ER, respectively. Performance benefits were quantified in terms of the bandwidth reduction ratio BR=N/I obtained from the comparison of the OneSAF Testbed Semi-Automated Forces (OTBSAF) simulator under DIS requiring a total of N bits and a testbed modified for the Concurrent Model approach which required I bits. In the experiments conducted, a range of 100 d BR d 294 was obtained representing two orders of magnitude reduction in simulation traffic. Investigation showed that the models rely heavily on the priority data structure of the discrete event simulation and that performance of the overall simulation can be enhanced by an additional 6% by improving the queue management. A low run-time overhead, self-adapting storage policy called the Smart Priority Queue (SPQ) was developed and evaluated within the Concurrent Model. The proposed SPQ policies employ a lowcomplexity linear queue for near head activities and a rapid-indexing variable binwidth calendar queue for distant events. The SPQ configuration is determined by monitoring queue access behavior using cost scoring factors and then applying heuristics to adjust the organization of the underlying data structures. Results indicate that optimizing storage to the spatial distribution of queue access can decrease HOLD operation cost between 25% and 250% over existing algorithms such as calendar queues. Taken together, these techniques provide an entity state generation mechanism capable of overcoming the challenges of Embedded Simulation in harsh mobile communications environments with restricted bandwidth, increased message latency, and extended message drop-outs.Ph.D.
Department of Electrical and Computer Engineering
Engineering and Computer Science
Computer Engineering
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Venkateswaran, Jayendran. "PRODUCTION AND DISTRIBUTION PLANNING FOR DYNAMIC SUPPLY CHAINS USING MULTI-RESOLUTION HYBRID MODELS." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1185%5F1%5Fm.pdf&type=application/pdf.
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Sengupta, Aritra. "Empirical Hierarchical Modeling and Predictive Inference for Big, Spatial, Discrete, and Continuous Data." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1350660056.
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Blythe, Kevin S. "A methodology of aggregating discrete microscopic traffic data for macroscopic model calibration and nonequilibrium visual detection purposes." Ohio : Ohio University, 1991. http://www.ohiolink.edu/etd/view.cgi?ohiou1183653868.
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Cheon, Saehoon. "Experimental Frame Structuring For Automated Model Construction: Application to Simulated Weather Generation." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/195473.
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The source system is the real or virtual environment that we are interested in modeling. It is viewed as a source of observable data, in the form of time-indexed trajectories of variables. The data that has been gathered from observing or experimenting with a system is called the system behavior data base. The time indexed trajectories of variables provide an important clue to compose the DEVS (discrete event specification) model. Once event set is derived from the time indexed trajectories of variable, the DEVS model formalism can be extracted from the given event set. The process must not be a simple model generation but a meaningful model structuring of a request. The source data and query designed with SES are converted to XML Meta data by XML converting process. The SES serves as a compact representation for organizing all possible hierarchical composition of system so that it performs an important role to design the structural representation of query and source data to be saved. For the real data application, the model structuring with the US Climate Normals is introduced. Moreover, complex systems are able to be developed at different levels of resolution. When the huge size of source data in US Climate Normals are implemented for the DEVS model, the model complexity is unavoidable. This issue is dealt with the creation of the equivalent lumped model based on the concept of morphism. Two methods to define the resolution level are discussed, fixed and dynamic definition. Aggregation is also discussed as the one of approaches for the model abstraction. Finally, this paper will introduce the process to integrate the DEVSML(DEVS Modeling Language) engine with the DEVS model creation engine for the Web Service Oriented Architecture.
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Pucciarelli, Amilcar Jose. "Modelagem de series temporais discretas utilizando modelo nebuloso Takagi-Sugeno." [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/258790.
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Orientador: Gilmar BarretoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Este estudo primeiramente investiga fundamentos teóricos para análise, desenvolvimento e implementação de algoritmos para modelagem de dados de sistemas dinâmicos e de séries temporais com a finalidade de predição. As séries temporais utilizadas são baseadas em dados reais retirados da literatura. A grande vantagem de se modelar uma série temporal e de se prever um dado futuro é poder tomar ações antecipadas sobre ela o quem vem a ser muito útil, por exemplo em controle. O modelo nebuloso Takagi-Sugeno será utilizado na modelagem das séries temporais onde os conjuntos nebulosos do antecedente e os parâmetros do conseqüente são estimados via métodos de agrupamentos e identificação paramétrica, respectivamente
Abstract: This work firstly explores theoretical foundations for analisis, development and implementation of algorithms for data modelling dynamic systems and time series with a prediction goaI. The used time series are based on real data from the literature. The main advantage of time series modelling and forecasting is make antecipated decisions about it, and this becomes very useful, for example, in controI. The Takagi-Sugeno fuzzy model is used for time series modelling where the antecedent fuzzy partitions and the consequent parameters are estimated by clustering methods and parametric identification, respectively
Mestrado
Automação
Mestre em Engenharia Elétrica
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Fernandez, Gonzalez Ramon Francisco. "Avaliação microeconômica do comportamento de investidores frente às alterações de condições de mercado: os determinantes da não racionalidade dos investidores no mercado de fundos brasileiros." reponame:Repositório Institucional do FGV, 2015. http://hdl.handle.net/10438/16495.
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In this paper we seek to identify the determinants of demand for mutual funds in Brazil through the logit model, which is widely used in the theory of industrial organizations. Whenever possible we perform 'links' with the main concepts of behavioral finance. Thus, we clarify the main variables that impact variations of 'market share' in the mutual funds industry. We conclude that the main indicators observed by investors at the time of decision-making, are the CDI, inflation, the real interest rate, the variation of the dollar and the stock market, on the other hand the accumulated return of the last three months is factor decisive for investors to apply or redeem an investment fund. Risk variables and expected return we thought to have a strong impact, not significant for variations of 'share'.
Neste trabalho buscamos identificar os principais determinantes da demanda por fundos de investimento no Brasil através do modelo Logit, que é bastante utilizado na teoria das organizações industriais. Sempre que possível realizamos 'links' com os principais conceitos de finanças comportamentais. Assim, conseguimos aclarar as principais variáveis que impactam as variações de 'market-share' na indústria de fundos de investimento. Concluímos que os principais indicadores observados pelos investidores no momento de tomada de decisão são o CDI, a inflação, a taxa real de juros, a variação do dólar e da bolsa de valores, por outro lado a rentabilidade acumulada dos últimos três meses é fator decisivo para que o investidor aplique ou resgate um fundo de investimento. Variáveis de risco e de retorno esperado que imaginávamos ter forte impacto, não se mostraram significativas para as variações de 'share'.
En este trabajo buscamos identificar los determinantes de la demanda de los principales fondos de inversión en Brasil através del modelo Logit, que es ampliamente utilizado en la teoría de las organizaciones industriales. Siempre que posible hemos realizado 'links' con los principales conceptos de las finanzas comportamentales. Por lo tanto, fue posible aclarar las principales variables a que las variaciones de impacto de 'cuota de mercado' en la industria de fondos de inversión. Llegamos a la conclusión de que los principales indicadores observados por los inversores en el momento de la toma de decisiones, es el CDI, la inflación, la tasa de interés real, la variación del dólar y el mercado de valores, por otro lado, la rentabilidad acumulada de los últimos tres meses es un factor decisiva para que los inversionistas invirtan o salgan de un fondo de inversión. Las variables de riesgo y rendimiento esperado que pensabamos tener un impacto fuerte, no se demonstraran significativas para las variaciones de las cuotas de mercado.
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Shay, Nathan Michael. "Investigating Real-Time Employer-Based Ridesharing Preferences Based on Stated Preference Survey Data." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471587439.
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Ulin, Samuel. "Digging deep : A data-driven approach to model reduction in a granular bulldozing scenario." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-152498.
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The current simulation method for granular dynamics used by the physics engine AGX Dynamics is a nonsmooth variant of the popular Discrete Element Method (DEM). While powerful, there is a need for close to real time simulations of a higher spatial resolution than currently possible. In this thesis a data-driven model reduction approach using machine learning was considered. A data-driven simulation pipeline was presented and partially implemented. The method consists of sampling the velocity and density field of the granular particles and teaching a machine learning algorithm to predict the particles' interaction with a bulldozer blade as well as predicting the time evolution of its velocity field. A procedure for producing training scenarios and training data for the machine learning algorithm was implemented as well as several machine learning algorithms; a linear regressor, a multilayer perceptron and a convolutional neural network. The results showed that the method is promising, however further work will need to show whether or not the pipeline is feasible to implement in a simulation.
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Pires, dos Santos Rebecca. "The Application of Artificial Neural Networks for Prioritization of Independent Variables of a Discrete Event Simulation Model in a Manufacturing Environment." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6431.
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The high complexity existent in businesses has required managers to rely on accurate and up to date information. Over the years, many tools have been created to give support to decision makers, such as discrete event simulation and artificial neural networks. Both tools have been applied to improve business performance; however, most of the time they are used separately. This research aims to interpret artificial neural network models that are applied to the data generated by a simulation model and determine which inputs have the most impact on the output of a business. This would allow prioritization of the variables for maximized system performance. A connection weight approach will be used to interpret the artificial neural network models. The research methodology consisted of three main steps: 1) creation of an accurate simulation model, 2) application of artificial neural network models to the output data of the simulation model, and 3) interpretation of the artificial neural network models using the connection weight approach. In order to test this methodology, a study was performed in the raw material receiving process of a manufacturing facility aiming to determine which variables impact the most the total time a truck stays in the system waiting to unload its materials. Through the research it was possible to observe that artificial neural network models can be useful in making good prediction about the system they model. Moreover, through the connection weight approach, artificial neural network models were interpreted and helped determine the variables that have the greatest impact on the modeled system. As future research, it would be interesting to use this methodology with other data mining algorithms and understand which techniques have the greatest capabilities of determining the most meaningful variables of a model. It would also be relevant to use this methodology as a resource to not only prioritize, but optimize a simulation model.