Open Access
2012 Adaptive-modal Bayesian nonparametric regression
George Karabatsos, Stephen G. Walker
Electron. J. Statist. 6: 2038-2068 (2012). DOI: 10.1214/12-EJS738


We introduce a novel, Bayesian nonparametric, infinite-mixture regression model. The model has unimodal kernel (component) densities, and has covariate-dependent mixture weights that are defined by an infinite ordered-category probits regression. Based on these mixture weights, the regression model predicts a probability density that becomes increasingly unimodal as the explanatory power of the covariate (vector) increases, and increasingly multimodal as this explanatory power decreases, while allowing the explanatory power to vary from one covariate (vector) value to another. The model is illustrated and compared against many other regression models in terms of predictive performance, through the analysis of many real and simulated data sets.


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George Karabatsos. Stephen G. Walker. "Adaptive-modal Bayesian nonparametric regression." Electron. J. Statist. 6 2038 - 2068, 2012.


Published: 2012
First available in Project Euclid: 2 November 2012

zbMATH: 1335.62051
MathSciNet: MR3020256
Digital Object Identifier: 10.1214/12-EJS738

Keywords: Bayesian inference , binary regression , Nonparametric regression , unimodal distribution

Rights: Copyright © 2012 The Institute of Mathematical Statistics and the Bernoulli Society

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