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Bayesian Quantile Regression in Late...

Wang, Yifan,

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Bayesian Quantile Regression in Latent Variable Models /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian Quantile Regression in Latent Variable Models // Yifan Wang.
作者:	Wang, Yifan,
面頁冊數:	1 electronic resource (115 pages)
附註:	Source: Dissertations Abstracts International, Volume: 78-08, Section: B.
Contained By:	Dissertations Abstracts International78-08B.
標題:	Statistics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10307359
ISBN:	9781369519334

Bayesian Quantile Regression in Latent Variable Models /
Wang, Yifan,

Bayesian Quantile Regression in Latent Variable Models /Yifan Wang. - 1 electronic resource (115 pages)

Source: Dissertations Abstracts International, Volume: 78-08, Section: B.

Quantile regression is a popular method that has been introduced to a wide range of models in statistics and econometrics. Compared to the mean regression, quantile regression provides a complete description of the relationship between the response and the predictors. However, despite its popularity, quantile regression has been rarely studied in latent variable models, which are useful in the analysis of variables that cannot be directly measured. In this thesis, the Bayesian quantile regression approach based upon the asymmetric Laplace distribution is developed in two kinds of latent variable models, including structural equation models (SEMs) and varying coefficient models with random effects, which have not been proposed before. Apart from coefficients estimation, the variable selection using regularization methods, including Bayesian Lasso and Bayesian adaptive Lasso, is also considered. Full Markov Chain Monte Carlo (MCMC) algorithms with Gibbs sampling method are derived for the Bayesian inference. Simulation studies demonstrate that performance of the proposed methods are satisfactory. We also utilize the proposed methods in several real data examples in medical science and econometrics to provide a deeper insight of the interrelationship among the variables.

English

ISBN: 9781369519334Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Markov Chain Monte Carlo algorithms

Bayesian Quantile Regression in Latent Variable Models /
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520 $a Quantile regression is a popular method that has been introduced to a wide range of models in statistics and econometrics. Compared to the mean regression, quantile regression provides a complete description of the relationship between the response and the predictors. However, despite its popularity, quantile regression has been rarely studied in latent variable models, which are useful in the analysis of variables that cannot be directly measured. In this thesis, the Bayesian quantile regression approach based upon the asymmetric Laplace distribution is developed in two kinds of latent variable models, including structural equation models (SEMs) and varying coefficient models with random effects, which have not been proposed before. Apart from coefficients estimation, the variable selection using regularization methods, including Bayesian Lasso and Bayesian adaptive Lasso, is also considered. Full Markov Chain Monte Carlo (MCMC) algorithms with Gibbs sampling method are derived for the Bayesian inference. Simulation studies demonstrate that performance of the proposed methods are satisfactory. We also utilize the proposed methods in several real data examples in medical science and econometrics to provide a deeper insight of the interrelationship among the variables.
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653 $a Structural equation models
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