Bayesian Categorical Data Analysis

Ming-Hui Chen (), Dipak K. Dey (), Peter Müller (), Dongchu Sun () and Keying Ye ()
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Ming-Hui Chen: University of Connecticut, Department of Statistics
Dipak K. Dey: University of Connecticut, Department of Statistics
Peter Müller: The University of Texas, M. D. Anderson Cancer Center, Department of Biostatistics
Dongchu Sun: University of Missouri-Columbia, Department of Statistics
Keying Ye: University of Texas at San Antonio, Department of Management Science and Statistics, College of Business

Chapter Chapter 12 in Frontiers of Statistical Decision Making and Bayesian Analysis, 2010, pp 419-466 from Springer

Abstract: Abstract Some interesting research challenges for Bayesian inference arise from binary and categorical data, including more traditional inference problems like contingency tables with sparse data and case-control studies as well as more recent research frontiers like non-standard link function for binary data regression.

Keywords: Monte Carlo; Marginal Likelihood; Deviance Information Criterion; Markov Chain Monte Carlo Sampling; High Posterior Density (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4419-6944-6_12

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DOI: 10.1007/978-1-4419-6944-6_12

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