Relevant bibliographies by topics / Finite mixture of quantile regression

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  2. Dissertations / Theses
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Academic literature on the topic 'Finite mixture of quantile regression'

Author: Grafiati
Published: 22 February 2023

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Journal articles on the topic "Finite mixture of quantile regression"

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Alfò, Marco, Nicola Salvati, and M. Giovanna Ranallli. "Finite mixtures of quantile and M-quantile regression models." Statistics and Computing 27, no. 2 (2016): 547–70. http://dx.doi.org/10.1007/s11222-016-9638-1.

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Tian, Yuzhu, Manlai Tang, and Maozai Tian. "A class of finite mixture of quantile regressions with its applications." Journal of Applied Statistics 43, no. 7 (2015): 1240–52. http://dx.doi.org/10.1080/02664763.2015.1094035.

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Kiliç Depren, Serpil. "DETERMINATION OF THE FACTORS AFFECTING STUDENTS’ SCIENCE ACHIEVEMENT LEVEL IN TURKEY AND SINGAPORE: AN APPLICATION OF QUANTILE REGRESSION MIXTURE MODEL." Journal of Baltic Science Education 19, no. 2 (2020): 247–60. http://dx.doi.org/10.33225/jbse/20.19.247.

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In the last decade, the usage of advanced statistical models is growing rapidly in many different disciplines. However, the Quantile Regression Mixture Model (QRMIX), which is a developed approach of the Finite Mixture Model (FMM), is an applicable new method in the educational literature. The aim of the proposed study was to determine factors affecting students' science achievement using the QRMIX approach. To reach this aim, data of the Programme for International Student Assessment (PISA) survey, which has been conducted by the Organization Economic for Co-Operation and Development (OECD) every 3 years, was used. Dataset used in the research is composed of 6,115 students from Singapore, which is the top-performer country among the participant countries, and 5,895 students from Turkey. The results showed that the factors affecting students' science achievement and its importance on the achievement differentiated according to the achievement levels of the students. In conclusion, it was revealed that Turkish students with the lowest science achievement level should be supported with home possessions, perceived feedback, and environmental awareness and Singaporean students with the lowest achievement level should be supported with perceived feedback, enjoyment of science, and epistemological beliefs. Keywords: finite mixture models, Programme for International Student Assessment, quantile regression mixture models, science performance.
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Del Sarto, Simone, Maria Francesca Marino, Maria Giovanna Ranalli, and Nicola Salvati. "Using finite mixtures of M-quantile regression models to handle unobserved heterogeneity in assessing the effect of meteorology and traffic on air quality." Stochastic Environmental Research and Risk Assessment 33, no. 7 (2019): 1345–59. http://dx.doi.org/10.1007/s00477-019-01687-x.

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Liu, Xi, Keming Yu, Qifa Xu, and Xueqing Tang. "Improved local quantile regression." Statistical Modelling 19, no. 5 (2018): 501–23. http://dx.doi.org/10.1177/1471082x18782057.

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We investigate a new kernel-weighted likelihood smoothing quantile regression method. The likelihood is based on a normal scale-mixture representation of asymmetric Laplace distribution (ALD). This approach enjoys the same good design adaptation as the local quantile regression ( Spokoiny et al., 2013 , Journal of Statistical Planning and Inference, 143, 1109–1129), particularly for smoothing extreme quantile curves, and ensures non-crossing quantile curves for any given sample. The performance of the proposed method is evaluated via extensive Monte Carlo simulation studies and one real data analysis.
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Chen, Colin. "A Finite Smoothing Algorithm for Quantile Regression." Journal of Computational and Graphical Statistics 16, no. 1 (2007): 136–64. http://dx.doi.org/10.1198/106186007x180336.

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Chernozhukov, Victor, Christian Hansen, and Michael Jansson. "Finite sample inference for quantile regression models." Journal of Econometrics 152, no. 2 (2009): 93–103. http://dx.doi.org/10.1016/j.jeconom.2009.01.004.

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Alhamzawi, Rahim, and Keming Yu. "Power Prior Elicitation in Bayesian Quantile Regression." Journal of Probability and Statistics 2011 (2011): 1–16. http://dx.doi.org/10.1155/2011/874907.

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We address a quantile dependent prior for Bayesian quantile regression. We extend the idea of the power prior distribution in Bayesian quantile regression by employing the likelihood function that is based on a location-scale mixture representation of the asymmetric Laplace distribution. The propriety of the power prior is one of the critical issues in Bayesian analysis. Thus, we discuss the propriety of the power prior in Bayesian quantile regression. The methods are illustrated with both simulation and real data.
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Kalantan, Zakiah I., and Jochen Einbeck. "Quantile-Based Estimation of the Finite Cauchy Mixture Model." Symmetry 11, no. 9 (2019): 1186. http://dx.doi.org/10.3390/sym11091186.

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Heterogeneity and outliers are two aspects which add considerable complexity to the analysis of data. The Cauchy mixture model is an attractive device to deal with both issues simultaneously. This paper develops an Expectation-Maximization-type algorithm to estimate the Cauchy mixture parameters. The main ingredient of the algorithm are appropriately weighted component-wise quantiles which can be efficiently computed. The effectiveness of the method is demonstrated through a simulation study, and the techniques are illustrated by real data from the fields of psychology, engineering and computer vision.
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Zhang, Zhengyu. "LOCAL PARTITIONED QUANTILE REGRESSION." Econometric Theory 33, no. 5 (2016): 1081–120. http://dx.doi.org/10.1017/s0266466616000293.

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In this paper, we consider the nonparametric estimation of a broad class of quantile regression models, in which the partially linear, additive, and varying coefficient models are nested. We propose for the model a two-stage kernel-weighted least squares estimator by generalizing the idea of local partitioned mean regression (Christopeit and Hoderlein, 2006, Econometrica 74, 787–817) to a quantile regression framework. The proposed estimator is shown to have desirable asymptotic properties under standard regularity conditions. The new estimator has three advantages relative to existing methods. First, it is structurally simple and widely applicable to the general model as well as its submodels. Second, both the functional coefficients and their derivatives up to any given order can be estimated. Third, the procedure readily extends to censored data, including fixed or random censoring. A Monte Carlo experiment indicates that the proposed estimator performs well in finite samples. An empirical application is also provided.
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Dissertations / Theses on the topic "Finite mixture of quantile regression"

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Sánchez, Luis Enrique Benites. "Finite mixture of regression models." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-10052018-131627/.

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This dissertation consists of three articles, proposing extensions of finite mixtures in regression models. Here we consider a flexible class of both univariate and multivariate distributions, which allow adequate modeling of asymmetric data that have multimodality, heavy tails and outlying observations. This class has special cases such as skew-normal, skew-t, skew-slash and skew normal contaminated distributions, as well as symmetric cases. Initially, a model is proposed based on the assumption that the errors follow a finite mixture of scale mixture of skew-normal (FM-SMSN) distribution rather than the conventional normal distribution. Next, we have a censored regression model where we consider that the error follows a finite mixture of scale mixture of normal (SMN) distribution. Next, we propose a censored regression model where we consider that the error follows a finite mixture of scale mixture of normal (SMN) distribution. Finally, we consider a finite mixture of multivariate regression where the error has a multivariate SMSN distribution. For all proposed models, two R packages were developed, which are reported in the appendix.<br>Esta tese composta por três artigos, visa propor extensões das misturas finitas nos modelos de regressão. Aqui vamos considerar uma classe flexível de distribuições tanto univariada como multivariada, que permitem modelar adequadamente dados assimmétricos, que presentam multimodalidade, caldas pesadas e observações atípicas. Esta classe possui casos especiais tais como as distribuições skew-normal, skew-t, skew slash, skew normal contaminada, assim como os casos simétricos. Inicialmente, é proposto um modelo baseado na suposição de que os erros seguem uma mistura finita da distribuição mistura de escala skew-normal (SMSN) ao invés da convencional distribuição normal. Em seguida, temos um modelo de regressão censurado onde consideramos que o erro segue uma mistura finita da distribuição da mistura de escala normal (SMN). E por último, é considerada um mistura finita de regressão multivariada onde o erro tem uma distribuição SMSN multivariada. Para todos os modelos propostos foram desenvolvidos dois pacotes do software R, que estão exemplificados no apêndice.
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Qarmalah, Najla Mohammed A. "Finite mixture models : visualisation, localised regression, and prediction." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12486/.

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Initially, this thesis introduces a new graphical tool, that can be used to summarise data possessing a mixture structure. Computation of the required summary statistics makes use of posterior probabilities of class membership obtained from a fitted mixture model. In this context, both real and simulated data are used to highlight the usefulness of the tool for the visualisation of mixture data in comparison to the use of a traditional boxplot. This thesis uses localised mixture models to produce predictions from time series data. Estimation method used in these models is achieved using a kernel-weighted version of an EM-algorithm: exponential kernels with different bandwidths are used as weight functions. By modelling a mixture of local regressions at a target time point, but using different bandwidths, an informative estimated mixture probabilities can be gained relating to the amount of information available in the data set. This information is given a scale of resolution, that corresponds to each bandwidth. Nadaraya-Watson and local linear estimators are used to carry out localised estimation. For prediction at a future time point, a new methodology of bandwidth selection and adequate methods are proposed for each local method, and then compared to competing forecasting routines. A simulation study is executed to assess the performance of this model for prediction. Finally, double-localised mixture models are presented, that can be used to improve predictions for a variable time series using additional information provided by other time series. Estimation for these models is achieved using a double-kernel-weighted version of the EM-algorithm, employing exponential kernels with different horizontal bandwidths and normal kernels with different vertical bandwidths, that are focused around a target observation at a given time point. Nadaraya-Watson and local linear estimators are used to carry out the double-localised estimation. For prediction at a future time point, different approaches are considered for each local method, and are compared to competing forecasting routines. Real data is used to investigate the performance of the localised and double-localised mixture models for prediction. The data used predominately in this thesis is taken from the International Energy Agency (IEA).
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Li, Xiongya. "Robust multivariate mixture regression models." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/38427.

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Doctor of Philosophy<br>Department of Statistics<br>Weixing Song<br>In this dissertation, we proposed a new robust estimation procedure for two multivariate mixture regression models and applied this novel method to functional mapping of dynamic traits. In the first part, a robust estimation procedure for the mixture of classical multivariate linear regression models is discussed by assuming that the error terms follow a multivariate Laplace distribution. An EM algorithm is developed based on the fact that the multivariate Laplace distribution is a scale mixture of the multivariate standard normal distribution. The performance of the proposed algorithm is thoroughly evaluated by some simulation and comparison studies. In the second part, the similar idea is extended to the mixture of linear mixed regression models by assuming that the random effect and the regression error jointly follow a multivariate Laplace distribution. Compared with the existing robust t procedure in the literature, simulation studies indicate that the finite sample performance of the proposed estimation procedure outperforms or is at least comparable to the robust t procedure. Comparing to t procedure, there is no need to determine the degrees of freedom, so the new robust estimation procedure is computationally more efficient than the robust t procedure. The ascent property for both EM algorithms are also proved. In the third part, the proposed robust method is applied to identify quantitative trait loci (QTL) underlying a functional mapping framework with dynamic traits of agricultural or biomedical interest. A robust multivariate Laplace mapping framework was proposed to replace the normality assumption. Simulation studies show the proposed method is comparable to the robust multivariate t-distribution developed in literature and outperforms the normal procedure. As an illustration, the proposed method is also applied to a real data set.
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Zhang, Dengfeng. "Latent Class Model in Transportation Study." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/51203.

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Statistics, as a critical component in transportation research, has been widely used to analyze driver safety, travel time, traffic flow and numerous other problems. Many of these popular topics can be interpreted as to establish the statistical models for the latent structure of data. Over the past several years, the interest in latent class models has continuously increased due to their great potential in solving practical problems. In this dissertation, I developed several latent class models to quantitatively analyze the hidden structure of transportation data and addressed related application issues. The first model is focused on the uncertainty of travel time, which is critical for assessing the reliability of transportation systems. Travel time is random in nature, and contains substantial variability, especially under congested traffic conditions. A Bayesian mixture model, with the ability to incorporate the influence from covariates such as traffic volume, has been proposed. This model advances the previous multi-state travel time reliability model in which the relationship between response and predictors was lacking. The Bayesian mixture travel time model, however, lack the power to accurately predict the future travel time. The analysis indicates that the independence assumption, which is difficult to justify in real data, could be a potential issue. Therefore, I proposed a Hidden Markov model to accommodate dependency structure, and the modeling results were significantly improved. The second and third parts of the dissertation focus on the driver safety identification. Given the demographic information and crash history, the number of crashes, as a type of count data, is commonly modeled by Poisson regression. However, the over-dispersion issue within the data implies that a single Poisson distribution is insufficient to depict the substantial variability. Poisson mixture model is proposed and applied to identify risky and safe drivers. The lower bound of the estimated misclassification rate is evaluated using the concept of overlap probability. Several theoretical results have been discussed regarding the overlap probability. I also introduced quantile regression based on discrete data to specifically model the high-risk drivers. In summary, the major objective of my research is to develop latent class methods and explore the hidden structure within the transportation data, and the approaches I employed can also be implemented for similar research questions in other areas.<br>Ph. D.
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Schoen, Stephanie. "Individual and Cumulative Effects of a Mixture of Phthalates and Children's Intellectual Abilities: A Secondary Analysis of Data from the MIREC Study." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42674.

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Phthalates, chemicals found in a variety of consumer goods and personal care products, may adversely affect fetal neurodevelopment. Women are exposed to a mixture of phthalates during pregnancy because of the common presence of these chemicals in consumer goods. The aim of this study is to investigate potential associations between phthalate exposure during the first trimester of gestation and Intelligence Quotient (IQ) scores of 3-year old children.
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Leisch, Friedrich. "FlexMix: A general framework for finite mixture models and latent class regression in R." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/712/1/document.pdf.

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Flexmix implements a general framework for fitting discrete mixtures of regression models in the R statistical computing environment: three variants of the EM algorithm can be used for parameter estimation, regressors and responses may be multivariate with arbitrary dimension, data may be grouped, e.g., to account for multiple observations per individual, the usual formula interface of the S language is used for convenient model specification, and a modular concept of driver functions allows to interface many di_erent types of regression models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering. Flexmix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. (author's abstract)<br>Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Xu, Li. "Statistical Methods for Variability Management in High-Performance Computing." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104184.

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High-performance computing (HPC) variability management is an important topic in computer science. Research topics include experimental designs for efficient data collection, surrogate models for predicting the performance variability, and system configuration optimization. Due to the complex architecture of HPC systems, a comprehensive study of HPC variability needs large-scale datasets, and experimental design techniques are useful for improved data collection. Surrogate models are essential to understand the variability as a function of system parameters, which can be obtained by mathematical and statistical models. After predicting the variability, optimization tools are needed for future system designs. This dissertation focuses on HPC input/output (I/O) variability through three main chapters. After the general introduction in Chapter 1, Chapter 2 focuses on the prediction models for the scalar description of I/O variability. A comprehensive comparison study is conducted, and major surrogate models for computer experiments are investigated. In addition, a tool is developed for system configuration optimization based on the chosen surrogate model. Chapter 3 conducts a detailed study for the multimodal phenomena in I/O throughput distribution and proposes an uncertainty estimation method for the optimal number of runs for future experiments. Mixture models are used to identify the number of modes for throughput distributions at different configurations. This chapter also addresses the uncertainty in parameter estimation and derives a formula for sample size calculation. The developed method is then applied to HPC variability data. Chapter 4 focuses on the prediction of functional outcomes with both qualitative and quantitative factors. Instead of a scalar description of I/O variability, the distribution of I/O throughput provides a comprehensive description of I/O variability. We develop a modified Gaussian process for functional prediction and apply the developed method to the large-scale HPC I/O variability data. Chapter 5 contains some general conclusions and areas for future work.<br>Doctor of Philosophy<br>This dissertation focuses on three projects that are all related to statistical methods in performance variability management in high-performance computing (HPC). HPC systems are computer systems that create high performance by aggregating a large number of computing units. The performance of HPC is measured by the throughput of a benchmark called the IOZone Filesystem Benchmark. The performance variability is the variation among throughputs when the system configuration is fixed. Variability management involves studying the relationship between performance variability and the system configuration. In Chapter 2, we use several existing prediction models to predict the standard deviation of throughputs given different system configurations and compare the accuracy of predictions. We also conduct HPC system optimization using the chosen prediction model as the objective function. In Chapter 3, we use the mixture model to determine the number of modes in the distribution of throughput under different system configurations. In addition, we develop a model to determine the number of additional runs for future benchmark experiments. In Chapter 4, we develop a statistical model that can predict the throughout distributions given the system configurations. We also compare the prediction of summary statistics of the throughput distributions with existing prediction models.
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Gogonel, Adriana Geanina. "Statistical Post-Processing Methods And Their Implementation On The Ensemble Prediction Systems For Forecasting Temperature In The Use Of The French Electric Consumption." Phd thesis, Université René Descartes - Paris V, 2012. http://tel.archives-ouvertes.fr/tel-00798576.

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The thesis has for objective to study new statistical methods to correct temperature predictionsthat may be implemented on the ensemble prediction system (EPS) of Meteo France so toimprove its use for the electric system management, at EDF France. The EPS of Meteo Francewe are working on contains 51 members (forecasts by time-step) and gives the temperaturepredictions for 14 days. The thesis contains three parts: in the first one we present the EPSand we implement two statistical methods improving the accuracy or the spread of the EPS andwe introduce criteria for comparing results. In the second part we introduce the extreme valuetheory and the mixture models we use to combine the model we build in the first part withmodels for fitting the distributions tails. In the third part we introduce the quantile regressionas another way of studying the tails of the distribution.
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Falk, Matthew Gregory. "Incorporating uncertainty in environmental models informed by imagery." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/33235/1/Matthew_Falk_Thesis.pdf.

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In this thesis, the issue of incorporating uncertainty for environmental modelling informed by imagery is explored by considering uncertainty in deterministic modelling, measurement uncertainty and uncertainty in image composition. Incorporating uncertainty in deterministic modelling is extended for use with imagery using the Bayesian melding approach. In the application presented, slope steepness is shown to be the main contributor to total uncertainty in the Revised Universal Soil Loss Equation. A spatial sampling procedure is also proposed to assist in implementing Bayesian melding given the increased data size with models informed by imagery. Measurement error models are another approach to incorporating uncertainty when data is informed by imagery. These models for measurement uncertainty, considered in a Bayesian conditional independence framework, are applied to ecological data generated from imagery. The models are shown to be appropriate and useful in certain situations. Measurement uncertainty is also considered in the context of change detection when two images are not co-registered. An approach for detecting change in two successive images is proposed that is not affected by registration. The procedure uses the Kolmogorov-Smirnov test on homogeneous segments of an image to detect change, with the homogeneous segments determined using a Bayesian mixture model of pixel values. Using the mixture model to segment an image also allows for uncertainty in the composition of an image. This thesis concludes by comparing several different Bayesian image segmentation approaches that allow for uncertainty regarding the allocation of pixels to different ground components. Each segmentation approach is applied to a data set of chlorophyll values and shown to have different benefits and drawbacks depending on the aims of the analysis.
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SABBI, ALBERTO. "Mixed effect quantile and M-quantile regression for spatial data." Doctoral thesis, 2020. http://hdl.handle.net/11573/1456341.

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Observed data are frequently characterized by a spatial dependence; that is the observed values can be influenced by the "geographical" position. In such a context it is possible to assume that the values observed in a given area are similar to those recorded in neighboring areas. Such data is frequently referred to as spatial data and they are frequently met in epidemiological, environmental and social studies, for a discussion see Haining, (1990). Spatial data can be multilevel, with samples being composed of lower level units (population, buildings) nested within higher level units (census tracts, municipalities, regions) in a geographical area. Green and Richardson (2002) proposed a general approach to modelling spatial data based on finite mixtures with spatial constraints, where the prior probabilities are modelled through a Markov Random Field (MRF) via a Potts representation (Kindermann and Snell, 1999, Strauss, 1977). This model was defined in a Bayesian context, assuming that the interaction parameter for the Potts model is fixed over the entire analyzed region. Geman and Geman (1984) have shown that this class process can be modelled by a Markov Random Field (MRF). As proved by the Hammersley-Clifford theorem, modelling the process through a MRF is equivalent to using a Gibbs distribution for the membership vector. In other words, the spatial dependence between component indicators is captured by a Gibbs distribution, using a representation similar to the Potts model discussed by Strauss (1977). In this work, a Gibbs distribution, with a component specific intercept and a constant interaction parameter, as in Green and Richardson (2002), is proposed to model effect of neighboring areas. This formulation allows to have a parameter specific to each component and a constant spatial dependence in the whole area, extending to quantile and m-quantile regression the proposed by Alfò et al. (2009) who suggested to have both intercept and interaction parameters depending on the mixture component, allowing for different prior probability and varying strength of spatial dependence. We propose, in the current dissertation to adopt this prior distribution to define a Finite mixture of quantile regression model (FMQRSP) and a Finite mixture of M-quantile regression model (FMMQSP), for spatial data.
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Books on the topic "Finite mixture of quantile regression"

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Chernozhukov, Victor. Finite sample inference for quantile regression models. Massachusetts Institute of Technology, Dept. of Economics, 2006.

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Dunson, David. Flexible Bayes regression of epidemiologic data. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.1.

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This article focuses on flexible Bayes regression of epidemiologic data involving pregnancy outcomes. It first provides an overview of finite mixture models and nonparametric Bayes methods before discussing some of the possibilities focusing on gestational age at delivery, DDE and age data from the Longnecker et al. (2001) study. More specifically, it examines how risk of premature delivery is impacted by maternal exposure to the pesticide DDT. The results showcase the use of Bayesian analysis in epidemiological studies that collect continuous health outcomes data, and in which the scientific and clinical interest typically focuses on the relationships between exposures and risks of an abnormal response, corresponding to an observation in the tails of the distribution. The article also highlights the limitations of current standard approaches that can be overcome by means of Bayesian analysis using density regression, mixtures and nonparametric models, as developed and applied in this pregnancy outcome study.
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Book chapters on the topic "Finite mixture of quantile regression"

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Lachos Dávila, Víctor Hugo, Celso Rômulo Barbosa Cabral, and Camila Borelli Zeller. "Mixture Regression Modeling Based on SMSN Distributions." In Finite Mixture of Skewed Distributions. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98029-4_6.

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Wüthrich, Mario V., and Michael Merz. "Selected Topics in Deep Learning." In Springer Actuarial. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_11.

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AbstractThis chapter presents a selection of different topics. We discuss forecasting under model uncertainty, deep quantile regression, deep composite regression and the LocalGLMnet which is an interpretable FN network architecture. Moreover, we provide a bootstrap example to assess prediction uncertainty, we discuss mixture density networks, and we give an outlook to studying variational inference.
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Rocci, Roberto, Roberto Di Mari, and Stefano Antonio Gattone. "Penalized Estimation of a Finite Mixture of Linear Regression Models." In Building Bridges between Soft and Statistical Methodologies for Data Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15509-3_43.

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Grün, Bettina, and Friedrich Leisch. "Testing for Genuine Multimodality in Finite Mixture Models: Application to Linear Regression Models." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70981-7_24.

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Di Mari, Roberto, Roberto Rocci, and Stefano Antonio Gattone. "Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42972-4_23.

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"Finite mixture distributions." In Flexible Regression and Smoothing. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b21973-7.

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Xia, Ye-Mao, Qi-Hang Zhu, and Jian-Wei Gou. "Assessing Heterogeneity of Two-Part Model via Bayesian Model-Based Clustering with Its Application to Cocaine Use Data." In Artificial Intelligence. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.103089.

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The purpose of this chapter is to provide an introduction to the model-based clustering within the Bayesian framework and apply it to asses the heterogeneity of fractional data via finite mixture two-part regression model. The problems related to the number of clusters and the configuration of observations are addressed via Markov Chains Monte Carlo (MCMC) sampling method. Gibbs sampler is implemented to draw observations from the related full conditionals. As a concrete example, the cocaine use data are analyzed to illustrate the merits of the proposed methodology.
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Conference papers on the topic "Finite mixture of quantile regression"

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Caeiro, Frederico, Ana P. Martins, and Inês J. Sequeira. "Finite sample behaviour of classical and quantile regression estimators for the Pareto distribution." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4912753.

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Zamani, Hossein, Pouya Faroughi, and Noriszura Ismail. "Estimation of count data using mixed Poisson, generalized Poisson and finite Poisson mixture regression models." In PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4882628.

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Cheng, Way Lee, Cai Shen, and Chia-fon F. Lee. "Application of Continuous Thermodynamics Method to Fuel Droplet Evaporation." In ASME 2012 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/icef2012-92177.

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A finite diffusion droplet evaporation model for complex liquid mixture composed of different homogeneous groups is presented in this paper. Separate distribution functions are used to describe the composition of each homogeneous group in the mixture. Only a few parameters are required to describe the mixture. Quasi-steady assumption is applied in the determination of evaporation rates and heat flux to the droplet, and the effects of surface regression, finite diffusion and preferential vaporization of the mixture are included in the liquid phase equations using an effective properties approach. A novel approach was used to reduce the transport equations for the liquid phase to a set of ordinary differential equations. The proposed model is capable in capturing the vaporization characteristics of complex liquid mixtures.
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Klisch, Stephen M., and Jeffrey C. Lotz. "Application of a Special Theory of Biphasic Mixtures to Annulus Fibrosus." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-0127.

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Abstract We applied a special theory of an incompressible mixture of an elastic solid and an inviscid fluid to experimental data (Iatridis et al., 1995) for axial confined compression of annulus fibrosus in finite deformation. We found that this special theory demands a significantly different nonlinear permeability function than the widely used theory of Kwan et al. (1990) and Holmes and Mow (1990) to achieve a best-fit regression to the surface stress time history.
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5

Jonsson, M., D. Charbonnier, P. Ott, and J. von Wolfersdorf. "Application of the Transient Heater Foil Technique for Heat Transfer and Film Cooling Effectiveness Measurements on a Turbine Vane Endwall." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50451.

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The paper presents an application of the transient heater foil measurement technique using thermochromic liquid crystals (TLC) to endwall heat transfer and film cooling investigations in a transonic turbine vane cascade. The film cooling configuration consists of an upstream slot, representing the leakage flow area between the interface of the combustor and the turbine, and several rows of cylindrical and fan-shaped holes within the passage. With the transient method chosen, the heat transfer and adiabatic film cooling effectiveness distributions can be obtained simultaneously together with the local heat release within the heater foil. Therefore, the heat release in the foil is not required to be uniform, and the foil can contain discrete holes in the film cooling configuration. Some new developments are presented, which are directed towards improved application of the transient heater foil method for such a complex configuration. This includes tailoring the foil heat-release distribution towards the expected heat transfer patterns, supported by numerical Finite-Element computations and the use of a double-TLC mixture for improved time-wise TLC indications. Additionally, CFD-simulations were used to evaluate the recovery temperature distribution through the vane cascade without film cooling. The experiments were performed in the linear cascade facility at the EPFL-Lausanne. A compressor provides a continuous air flow at near-ambient temperature regulated with heat exchangers. Carbon dioxide is used as coolant in order to achieve engine-representative density ratio between coolant and main flow. Multiple experiments with the same main and coolant flow settings but varying heat flux levels and coolant injection temperatures have been performed and simultaneously analysed using nonlinear regression analysis. The time required between successive experiments to return to homogenous initial conditions, as required by the transient method, has been analysed using an analytical solution for heat-on-heat-off conditions. This permits the model assumption of one-dimensional conduction within a semi-infinite wall with a heat releasing layer on the top. Example results for cases without cooling, with film cooling from rows of discrete holes and the addition of slot film cooling are used to illustrate the benefit of the new approaches for the investigated vane cascade.
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