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oai:www.ideals.illinois.edu:2142/20979 2023-07-10 col_2142_5131 col_2142_11614 com_2142_5130 com_2142_11613 com_2142_8903

Bockenholt, Ulf Yao, Kai-Ping Grace 2011-05-07T12:54:49Z 2011-05-07T12:54:49Z 10000-01-01 1995 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. Made available in DSpace on 2011-05-07T12:54:49Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9624544.pdf: 4758452 bytes, checksum: af2e90acce10ac875ff49196cde98805 (MD5) Previous issue date: 1995 Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:47:39Z Item is restricted indefinitely. Restriction data tranferred 2014-07-01T11:21:32-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission ETDs are only available to UIUC Users without author permission U of I Only AAI9624544 (UMI)AAI9624544 http://hdl.handle.net/2142/20979 eng Copyright 1995 Yao, Kai-Ping Grace Statistics Psychology, Psychometrics Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler text Psychology Psychology University of Illinois at Urbana-Champaign Dissertation Ph.D.