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Bayesian multivariate binary respons...

Hahn, Eugene David.

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Bayesian multivariate binary response models and their application to multiple response data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian multivariate binary response models and their application to multiple response data./
作者:	Hahn, Eugene David.
面頁冊數:	222 p.
附註:	Source: Dissertation Abstracts International, Volume: 62-02, Section: A, page: 0678.
Contained By:	Dissertation Abstracts International62-02A.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3004031
ISBN:	0493130128

Bayesian multivariate binary response models and their application to multiple response data.
Hahn, Eugene David.

Bayesian multivariate binary response models and their application to multiple response data. - 222 p.

Source: Dissertation Abstracts International, Volume: 62-02, Section: A, page: 0678.

Thesis (Ph.D.)--The George Washington University, 2001.

Advances in Markov chain Monte Carlo techniques have made the estimation of complex high-dimensional models more tractable, especially in the Bayesian approach to inference. One class of models that has been the subject of recent attention is the class of multivariate binary response models, of which the multivariate probit and the multivariate logit models are frequently encountered special cases. An important decision that arises when using these models is the selection of the link function. In previous work, Chambers and Cox (1967) identified situations in which the fitting of univariate binary response models was substantively impacted by the differential selection of the probit or the logit link function. However, little is known about how the choice of link function affects model fit in multivariate binary response models.

ISBN: 0493130128Subjects--Topical Terms:

517247
Statistics.

Bayesian multivariate binary response models and their application to multiple response data.
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520 $a Advances in Markov chain Monte Carlo techniques have made the estimation of complex high-dimensional models more tractable, especially in the Bayesian approach to inference. One class of models that has been the subject of recent attention is the class of multivariate binary response models, of which the multivariate probit and the multivariate logit models are frequently encountered special cases. An important decision that arises when using these models is the selection of the link function. In previous work, Chambers and Cox (1967) identified situations in which the fitting of univariate binary response models was substantively impacted by the differential selection of the probit or the logit link function. However, little is known about how the choice of link function affects model fit in multivariate binary response models.
520 $a New multivariate binary response models were proposed, and a Bayesian approach to the estimation of these models was adopted. Differences in model fit were predicted to be influenced by three factors: the presence of extreme independent variable levels, high dependent variable correlation, and sample size. Model fit was assessed using two methods of computing Bayes factors as well as the Deviance Information Criterion (DIC). A Monte Carlo study was conducted to examine the effects of these three factors.
520 $a The presence of both extreme independent variable levels and high dependent variable correlation were found to affect the difference in fit afforded by the two link functions. Additionally, it was found that marginal models and random effects models have different “preferred link functions”, for which model fit is more generally enhanced. The results with respect to the sample sizes that were considered in the current program of research were more equivocal.
520 $a As a follow-up study, the models were then estimated using a real-world marketing research data set. The findings from this analysis corroborated those of the Monte Carlo study.
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