Dirichlet Process Mixtures of Generalized Linear Models

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Lauren Hannah, David Blei, Warren Powell ;
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:313-320, 2010.

Abstract

We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLMs), a new method of nonparametric regression that accommodates continuous and categorical inputs, models a response variable locally by a generalized linear model. We give conditions for the existence and asymptotic unbiasedness of the DP-GLM regression mean function estimate; we then give a practical example for when those conditions hold. We evaluate DP-GLM on several data sets, comparing it to modern methods of nonparametric regression including regression trees and Gaussian processes.

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