Citation

Savitsky TD, Dalal SR. Appl. Stat. 2014; 63(4): 539-557.

Copyright

(Copyright © 2014, John Wiley and Sons)

DOI

10.1111/rssc.12049

PMID

unavailable

Abstract

Our application data are produced from a scalable, on-line expert elicitation process that incorporates hundreds of participating raters to score the importance of research goals for the prevention of suicide with the purpose of informing policy making. We develop a Bayesian formulation for analysis of ordinal multirater data motivated by our application. Our model employs a non-parametric mixture distribution over rater-indexed parameters for a latent continuous response under a Poisson-Dirichlet process mixing measure that allows inference about distinct rater behavioural and learning typologies from realized clusters. © 2014 The Royal Statistical Society and John Wiley & Sons Ltd 634 August 2014 10.1111/rssc.12049 Original Article Original Articles Published 2013.This article is a US Government work and is in the public domain in the USA..

Language: en

Keywords

Bayesian hierarchical models; Latent models; Markov chain Monte Carlo methods; Ordinal data; Poisson-Dirichlet process