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Bayesian Analysis of Order-Statistics Models for Ranking Data

Published online by Cambridge University Press:  01 January 2025

Philip L. H. Yu
Philip L. H. Yu*
Affiliation:
Department of Statistics and Actuarial Science, The University of Hong Kong
*
Requests for reprints should be sent to Philip L.H. Yu, Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong.
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Abstract

In this paper, a class of probability models for ranking data, the order-statistics models, is investigated. We extend the usual normal order-statistics model into one where the underlying random variables follow a multivariate normal distribution. Bayesian approach and the Gibbs sampling technique are used for parameter estimation. In addition, methods to assess the adequacy of model fit are introduced. Robustness of the model is studied by considering a multivariate-t distribution. The proposed method is applied to analyze the presidential election data of the American Psychological Association (APA).

Keywords

data augmentationGibbs samplingorder-statistics modelranking data
Type
Original Paper
Information
Psychometrika , Volume 65 , Issue 3 , September 2000 , pp. 281 - 299
Copyright
Copyright © 2000 The Psychometric Society

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Footnotes

The author is grateful to K. Lam, K.F. Lam, the Editor, an associate editor, and three reviewers for their valuable comments and suggestions. This research was substantially supported by the CRCG grant 335/017/0015 of the University of Hong Kong and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU 7169/98H). Upon completion of this paper, I became aware that similar work had been done independently by K.G. Yao and U. Böckenholt (1999).

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