Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler

Grace Yao*, Ulf Böckenholt

*Corresponding author for this work

Research output: Contribution to journalArticle

29 Scopus citations

Abstract

This paper presents a Gibbs sampler for the estimation of Thurstonian ranking models. This approach is useful for the analysis of ranking data with a large number of options. Approaches for assessing the goodness-of-fit of Thurstonian ranking models based on posterior predictive distributions are also discussed. Two simulation studies and two ranking studies are presented to illustrate that the Gibbs sampler is a promising solution to the numerical problems that previously plagued the estimation of Thurstonian ranking models.

Original languageEnglish (US)
Pages (from-to)79-92
Number of pages14
JournalBritish Journal of Mathematical and Statistical Psychology
Volume52
Issue number1
DOIs
StatePublished - Jan 1 1999

ASJC Scopus subject areas

  • Statistics and Probability
  • Arts and Humanities (miscellaneous)
  • Psychology(all)

Fingerprint Dive into the research topics of 'Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler'. Together they form a unique fingerprint.

  • Cite this