文章基本信息

标题：Adaptive-modal Bayesian nonparametric regression
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作者：George Karabatsos ; Stephen G. Walker
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2012
卷号：6
页码：2038-2068
DOI：10.1214/12-EJS738
语种：English
出版社：Institute of Mathematical Statistics
摘要：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.