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.
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Kottas, A., Gelfand, A.E. Modeling Variability Order: A Semiparametric Bayesian Approach. Methodology and Computing in Applied Probability 3, 427–442 (2001). https://doi.org/10.1023/A:1015420304825
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DOI: https://doi.org/10.1023/A:1015420304825