Data augmentation and parameter expansion for independent or spatially correlated ordinal data

Erin M. Schliep and Jennifer A. Hoeting

Computational Statistics & Data Analysis, 2015, vol. 90, issue C, 1-14

Abstract: Data augmentation and parameter expansion can lead to improved iterative sampling algorithms for Markov chain Monte Carlo (MCMC). Data augmentation allows for simpler and more feasible simulation from a posterior distribution. Parameter expansion accelerates convergence of iterative sampling algorithms by increasing the parameter space. Data augmentation and parameter-expanded data augmentation MCMC algorithms are proposed for fitting probit models for independent ordinal response data. The algorithms are extended for fitting probit linear mixed models for spatially correlated ordinal data. The effectiveness of data augmentation and parameter-expanded data augmentation is illustrated using the probit model and ordinal response data, however, the approach can be used broadly across model and data types.

Keywords: Data augmentation; Parameter expansion; Ordinal data; Probit model; Spatial correlation (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:90:y:2015:i:c:p:1-14

DOI: 10.1016/j.csda.2015.03.020

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