Relevant bibliographies by topics / Discrete data models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Discrete data models'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Discrete data models"

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Kianifard, Farid. "Models for Discrete Data." Technometrics 42, no. 3 (August 2000): 313–14. http://dx.doi.org/10.1080/00401706.2000.10486061.

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Williamson, John. "Models for Discrete Longitudinal Data." Journal of the American Statistical Association 101, no. 475 (September 2006): 1307. http://dx.doi.org/10.1198/jasa.2006.s117.

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Karlsson, Andreas. "Models for Discrete Longitudinal Data." Biometrics 62, no. 2 (June 2006): 628. http://dx.doi.org/10.1111/j.1541-0420.2006.00589_5.x.

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Paule, Ines, Pascal Girard, Gilles Freyer, and Michel Tod. "Pharmacodynamic Models for Discrete Data." Clinical Pharmacokinetics 51, no. 12 (October 17, 2012): 767–86. http://dx.doi.org/10.1007/s40262-012-0014-9.

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Bruni, Renato. "Discrete models for data imputation." Discrete Applied Mathematics 144, no. 1-2 (November 2004): 59–69. http://dx.doi.org/10.1016/j.dam.2004.04.004.

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Madigan, David, Jeremy York, and Denis Allard. "Bayesian Graphical Models for Discrete Data." International Statistical Review / Revue Internationale de Statistique 63, no. 2 (August 1995): 215. http://dx.doi.org/10.2307/1403615.

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Teugels, Jozef L., and Johan Van Horebeek. "Generalized graphical models for discrete data." Statistics & Probability Letters 38, no. 1 (May 1998): 41–47. http://dx.doi.org/10.1016/s0167-7152(97)00152-1.

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Karlis, Dimitris. "Book Review: Models for discrete data." Statistical Methods in Medical Research 10, no. 5 (October 2001): 367–68. http://dx.doi.org/10.1177/096228020101000507.

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Shinbrot, Marvin. "Discrete velocity models with small data." Meccanica 22, no. 1 (March 1987): 38–40. http://dx.doi.org/10.1007/bf01560124.

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Bouguila, Nizar, and Walid ElGuebaly. "Discrete data clustering using finite mixture models." Pattern Recognition 42, no. 1 (January 2009): 33–42. http://dx.doi.org/10.1016/j.patcog.2008.06.022.

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Dissertations / Theses on the topic "Discrete data models"

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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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Books on the topic "Discrete data models"

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Models for discrete data. Oxford: Clarendon Press, 1999.

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Coca, D. A direct approach to identification of nonlinear differential models from discrete data. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

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Chambers, Marcus J. On forecasting discrete data from continuous time models with an application to consumption. [Colchester]: University of Essex, Department of Economics, 1990.

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Tsang, K. M. Reconstruction of linear and nonlinear continuous time models from discrete time sampled-data systems. Sheffield: University of Sheffield, Dept. of Control Engineering, 1990.

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Aït-Sahalia, Yacine. Telling from discrete data whether the underlying continuous-time model is a diffusion. Cambridge, MA: National Bureau of Economic Research, 2001.

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Orme, Chris. A note on adjusting the bias of maximum likelihood estimators in discrete panel data models with unobserved random effects. Loughborough: Loughborough University of Technology, Department of Economics, 1992.

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Byun, Jae-Woong. Estimation of discrete dynamic models from endogenously-sampled company panel data: An analysis of direct investmentby Korean firms in the European Union. Leicester: University of Leicester, Department of Economics, 1994.

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Lenda, Grzegorz. Rozwinięcie metod tworzenia funkcji sklejanych w aspekcie budowy modeli na podstawie danych dyskretnych: Development of the methods to spline functions in terms of building models based on discrete data. Kraków: Wydawnictwa AGH, 2012.

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Correa, R., Inês Dutra, Mario Fiallos, and Fernando Gomes. Models for parallel and distributed computation: Theory, algorithmic techniques and applications. Boston, MA: Springer US, 2002.

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Jeannette, Janssen, and SpringerLink (Online service), eds. Algorithms and Models for the Web Graph: 9th International Workshop, WAW 2012, Halifax, NS, Canada, June 22-23, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Book chapters on the topic "Discrete data models"

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Qin, Jing. "Discrete Data Models." In Biased Sampling, Over-identified Parameter Problems and Beyond, 249–57. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4856-2_13.

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Kaeding, Matthias. "Discrete Time Models." In Bayesian Analysis of Failure Time Data Using P-Splines, 45–59. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-08393-9_4.

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Friendly, Michael, David Meyer, and Achim Zeileis. "Logistic Regression Models." In Discrete Data Analysis with R, 261–322. Boca Raton : Taylor & Francis, 2016. | Series: Chapman & hall/CRC texts in statistical science series ; 120 | “A CRC title.”: Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b19022-10.

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Friendly, Michael, David Meyer, and Achim Zeileis. "Extending Loglinear Models." In Discrete Data Analysis with R, 375–428. Boca Raton : Taylor & Francis, 2016. | Series: Chapman & hall/CRC texts in statistical science series ; 120 | “A CRC title.”: Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b19022-13.

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Santner, Thomas J., and Diane E. Duffy. "Loglinear Models." In The Statistical Analysis of Discrete Data, 113–41. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-1017-7_3.

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Punzo, Antonio. "Discrete Beta-Type Models." In Studies in Classification, Data Analysis, and Knowledge Organization, 253–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10745-0_27.

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Friendly, Michael, David Meyer, and Achim Zeileis. "Models for Polytomous Responses." In Discrete Data Analysis with R, 323–48. Boca Raton : Taylor & Francis, 2016. | Series: Chapman & hall/CRC texts in statistical science series ; 120 | “A CRC title.”: Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b19022-11.

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Ghosh, Sreeya, and Sumita Basu. "Hybrid Cellular Automata Models for Discrete Dynamical Systems." In Data Science and Data Analytics, 145–59. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003111290-8-10.

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Friendly, Michael, David Meyer, and Achim Zeileis. "Generalized Linear Models for Count Data." In Discrete Data Analysis with R, 429–504. Boca Raton : Taylor & Francis, 2016. | Series: Chapman & hall/CRC texts in statistical science series ; 120 | “A CRC title.”: Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b19022-14.

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Friendly, Michael, David Meyer, and Achim Zeileis. "Loglinear and Logit Models for Contingency Tables." In Discrete Data Analysis with R, 349–74. Boca Raton : Taylor & Francis, 2016. | Series: Chapman & hall/CRC texts in statistical science series ; 120 | “A CRC title.”: Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b19022-12.

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Conference papers on the topic "Discrete data models"

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Taniguchi, Tadanari, and Michio Sugeno. "Nonlinear Model Predictive Control Using Discrete-Time Piecewise Multilinear Models." In DSDE '21: 2021 4th International Conference on Data Storage and Data Engineering. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3456146.3456166.

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Wood, S. L. "Computing continuous models from discrete image data." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.94586.

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Hasilová, Kamila, and Gabriela Leflerová. "Continuous Models for Discrete Data of Residual Contamination." In Proceedings of the 31st European Safety and Reliability Conference. Singapore: Research Publishing Services, 2021. http://dx.doi.org/10.3850/978-981-18-2016-8_580-cd.

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Papadopoulou, Aleka A., and George M. Tsaklidis. "Discrete time semi-Markov models with fuzzy state space." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0025.

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Aldirawi, Hani, Jie Yang, and Ahmed A. Metwally. "Identifying Appropriate Probabilistic Models for Sparse Discrete Omics Data." In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2019. http://dx.doi.org/10.1109/bhi.2019.8834661.

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Labutov, Igor, Frans Schalekamp, Kelvin Luu, Hod Lipson, and Christoph Studer. "Optimally Discriminative Choice Sets in Discrete Choice Models." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939879.

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Du, Kang, Austin Goddard, and Yu Xiang. "On the Robustness of Causal Discovery with Additive Noise Models on Discrete Data." In 2020 Data Compression Conference (DCC). IEEE, 2020. http://dx.doi.org/10.1109/dcc47342.2020.00086.

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Makarau, Aliaksei, Gintautas Palubinskas, and Peter Reinartz. "Discrete Graphical Models for Alphabet-Based Multisensory Data Fusion and Classification." In 2011 International Symposium on Image and Data Fusion (ISIDF). IEEE, 2011. http://dx.doi.org/10.1109/isidf.2011.6024235.

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Roos, T., and Bin Yu. "Estimating sparse models from multivariate discrete data via transformed Lasso." In 2009 Information Theory and Applications Workshop (ITA). IEEE, 2009. http://dx.doi.org/10.1109/ita.2009.5044959.

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Agarwal, Deepak, and Srujana Merugu. "Predictive discrete latent factor models for large scale dyadic data." In the 13th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1281192.1281199.

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Reports on the topic "Discrete data models"

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Ait-Sahalia, Yacine. Telling from Discrete Data Whether the Underlying Continuous-Time Model is a Diffusion. Cambridge, MA: National Bureau of Economic Research, October 2001. http://dx.doi.org/10.3386/w8504.

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Bedoya-Maya, Felipe, Lynn Scholl, Orlando Sabogal-Cardona, and Daniel Oviedo. Who uses Transport Network Companies?: Characterization of Demand and its Relationship with Public Transit in Medellín. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003621.

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Transport Network Companies (TNCs) have become a popular alternative for mobility due to their ability to provide on-demand flexible mobility services. By offering smartphone-based, ride-hailing services capable of satisfying specific travel needs, these modes have transformed urban mobility worldwide. However, to-date, few studies have examined the impacts in the Latin American context. This analysis is a critical first step in developing policies to promote efficient and sustainable transport systems in the Latin-American region. This research examines the factors affecting the adoption of on-demand ride services in Medellín, Colombia. It also explores whether these are substituting or competing with public transit. First, it provides a descriptive analysis in which we relate the usage of platform-based services with neighborhood characteristics, socioeconomic information of individuals and families, and trip-level details. Next, factors contributing to the election of platform-based services modeled using discrete choice models. The results show that wealthy and highly educated families with low vehicle availability are more likely to use TNCs compared to other groups in Medellín. Evidence also points at gender effects, with being female significantly increasing the probability of using a TNC service. Finally, we observe both transit complementary and substitution patterns of use, depending on the context and by whom the service is requested.
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Harris, Jeffrey, and Sandra Sosa-Rubi. Impact of "Seguro Popular" on Prenatal Visits in Mexico, 2002-2005: Latent Class Model of Count Data with a Discrete Endogenous Variable. Cambridge, MA: National Bureau of Economic Research, May 2009. http://dx.doi.org/10.3386/w14995.

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Berney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman, and John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40401.

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The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.
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