Modeling Variability Order: A Semiparametric Bayesian Approach

Athanasios Kottas () and Alan E. Gelfand ()
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Athanasios Kottas: Duke University
Alan E. Gelfand: University of Connecticut

Methodology and Computing in Applied Probability, 2001, vol. 3, issue 4, 427-442

Abstract: Abstract In comparing two populations, sometimes a model incorporating a certain probability order is desired. In this setting, Bayesian modeling is attractive since a probability order restriction imposed a priori on the population distributions is retained a posteriori. Extending the work in Gelfand and Kottas (2001) for stochastic order specifications, we formulate modeling for distributions ordered in variability. We work with Dirichlet process mixtures resulting in a fully Bayesian semiparametric approach. The details for simulation-based model fitting and prior specification are provided. An example, based on two small subsets of time intervals between eruptions of the Old Faithful geyser, illustrates the methodology.

Keywords: Dirichlet process mixing; dispersion ordering; Markov chain Monte Carlo; sign changes (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1023/A:1015420304825

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