University of Illinois Urbana-Champaign

Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler

Yao, Kai-Ping Grace

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https://hdl.handle.net/2142/20979

Description

Title
Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler
Author(s)
Yao, Kai-Ping Grace
Issue Date
1995
Doctoral Committee Chair(s)
Bockenholt, Ulf
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Statistics
  • Psychology, Psychometrics
Language
eng
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.
Type of Resource
text
Permalink
http://hdl.handle.net/2142/20979
Copyright and License Information
Copyright 1995 Yao, Kai-Ping Grace

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Graduate Dissertations and Theses at Illinois PRIMARY
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Dissertations and Theses from the Dept. of Psychology