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Bayesian non‐parametric analysis of multirater ordinal data, with application to prioritizing research goals for prevention of suicide
Author(s) -
Savitsky Terrance D.,
Dalal Siddhartha R.
Publication year - 2014
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12049
Subject(s) - ordinal data , dirichlet process , bayesian probability , parametric statistics , psychology , bayesian inference , dirichlet distribution , inference , computer science , machine learning , econometrics , data mining , artificial intelligence , statistics , mathematics , mathematical analysis , boundary value problem
Summary 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.