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Title:Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler
Author(s):Yao, Kai-Ping Grace
Doctoral Committee Chair(s):Bockenholt, Ulf
Department / Program:Psychology
Degree Granting Institution:University of Illinois at Urbana-Champaign
Psychology, Psychometrics
Abstract:Thurstonian ranking models represent the psychological ranking process by latent random variables that follow a multivariate normal distribution. To evaluate the ranking probabilities and estimate the parameters of the ranking models, traditional approaches such as numerical integration methods are only feasible for ranking problems with a small number of objects. This paper presents a Bayesian approach to the estimation of the parameters of Thurstonian ranking models based on Gibbs sampling methods. Monte Carlo studies demonstrate that the Gibbs sampler is applicable to ranking problems with a large number of objects. To improve the efficiency of the Gibbs sampler for estimating constrained and unconstrained Thurstonian ranking models, two procedures, importance sampling and truncated multivariate normal simulation procedures, are investigated. In an application, rankings of ten objects from a study on compound preferences (McKeon, 1961) are analyzed.
Issue Date:1995
Rights Information:Copyright 1995 Yao, Kai-Ping Grace
Date Available in IDEALS:2011-05-07
Identifier in Online Catalog:AAI9624544
OCLC Identifier:(UMI)AAI9624544

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