{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/20979"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/20979","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Yao, Kai-Ping Grace"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Bockenholt, Ulf"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-07T12:54:49Z","date_published":"2011-05-07T12:54:49Z","updated_at":"2026-07-22T22:25:17Z","subjects":["Statistics","Psychology, Psychometrics"],"languages":["eng"],"rights":["Copyright 1995 Yao, Kai-Ping Grace"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9624544","(UMI)AAI9624544"],"render_values":[{"text":"AAI9624544","href":null,"code":true},{"text":"(UMI)AAI9624544","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/20979","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bockenholt, Ulf"]},{"key":"dc:creator","label":"Author","values":["Yao, Kai-Ping Grace"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-07T12:54:49Z","10000-01-01","1995"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Statistics","Psychology, Psychometrics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1995 Yao, Kai-Ping Grace"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9624544","(UMI)AAI9624544","http://hdl.handle.net/2142/20979"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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"]},{"key":"dc:title","label":"Title","values":["Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler"]}]}],"canonical_facts":{"dc:contributor":["Bockenholt, Ulf"],"dc:creator":["Yao, Kai-Ping Grace"],"dc:date":["2011-05-07T12:54:49Z","10000-01-01","1995"],"dc:description":["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). 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