Adaptive-modal Bayesian nonparametric regression
journal contributionposted on 2016-04-26, 00:00 authored by George Karabatsos, Stephen G. Walker
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.
This research is supported by National Science Foundation research grant SES- 1156372, from the program in Methodology, Measurement, and Statistics.
Publisher Statement© 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
PublisherInstitute of Mathematical Statistics