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Categorical Data

  • Chapter
Bayesian Nonparametric Data Analysis

Part of the book series: Springer Series in Statistics ((SSS))

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Abstract

We discuss nonparametric Bayesian methods that are suitable for inference with binary, ordinal and general categorical data. Modeling for such data becomes particularly interesting in the presence of covariates, when non- and semi-parametric Bayesian models can generalize the link function in a generalized linear model setup, the regression on covariates or both. An important application arises in inference for diagnostic screening and related inference for ROC (receiver-operator characteristic) curves. We include some discussion of a rapidly growing literature on non-parametric Bayesian inference for ROC curves.

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Authors and Affiliations

  1. Department of Mathematics, University of Texas at Austin, Austin, TX, USA

    Peter Müller

  2. Departamento de Estadística, Pontificia Universidad Católica, Santiago, Chile

    Fernando Andrés Quintana & Alejandro Jara

  3. Department of Statistics, University of South Carolina, Columbia, SC, USA

    Tim Hanson

Authors
  1. Peter Müller
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  2. Fernando Andrés Quintana
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  3. Alejandro Jara
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  4. Tim Hanson
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Müller, P., Quintana, F.A., Jara, A., Hanson, T. (2015). Categorical Data. In: Bayesian Nonparametric Data Analysis. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-18968-0_5

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