Dirichlet Process Mixtures of Generalized Linear Models

Details Dirichlet Process Mixtures of Generalized Linear Models Dirichlet Process Mixtures of Generalized Linear Models

2009-09-28

arxiv.org/abs/0909.5194v2

Statistics

Machine Learning

Scientific paper

We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We prove conditions for the asymptotic unbiasedness of the DP-GLM regression mean function estimate. We also give examples for when those conditions hold, including models for compactly supported continuous distributions and a model with continuous covariates and categorical response. We empirically analyze the properties of the DP-GLM and why it provides better results than existing Dirichlet process mixture regression models. We evaluate DP-GLM on several data sets, comparing it to modern methods of nonparametric regression like CART, Bayesian trees and Gaussian processes. Compared to existing techniques, the DP-GLM provides a single model (and corresponding inference algorithms) that performs well in many regression settings.

Affiliated with

Also associated with

No associations

Rating

Dirichlet Process Mixtures of Generalized Linear Models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Dirichlet Process Mixtures of Generalized Linear Models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Dirichlet Process Mixtures of Generalized Linear Models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-664012