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Adaptive-modal Bayesian nonparametric regression

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Title: Adaptive-modal Bayesian nonparametric regression
Author(s): Karabatsos, George; Walker, Stephen G.
Subject(s): Bayesian inference nonparametric regression unimodal distribution
Abstract: 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 mod- els in terms of predictive performance, through the analysis of many real and simulated data sets.
Issue Date: 2012
Publisher: Institute of Mathematical Statistics
Citation Info: Karabatsos, G. Adaptive-modal Bayesian nonparametric regression. Electronic Journal of Statistics. 2012. 6: 2038-2068. DOI: 10.1214/12-EJS738
Type: Article
Description: © 2012 by Institute of Mathematical Statistics, Electronic Journal of Statistics. The original publication is available at http://www.imstat.org/ejs/ DOI: 10.1214/12-EJS738
URI: http://hdl.handle.net/10027/20453
ISSN: 1935-7524
Sponsor: This research is supported by National Science Foundation research grant SES- 1156372, from the program in Methodology, Measurement, and Statistics.
Date Available in INDIGO: 2016-04-26
 

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