Dirichlet Process mixtures of Generalized Linear Models

Lauren A. Hannah, David M. Blei, Warren Buckler Powell

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLMs), a new method of nonparametric regression that accommodates continuous and categorical in-puts, models a response variable locally by a generalized linear model. We give conditions for the existence and asymptotic unbiased-ness 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.

Original languageEnglish (US)
Pages (from-to)313-320
Number of pages8
JournalJournal of Machine Learning Research
Volume9
StatePublished - Dec 1 2010
Event13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010 - Sardinia, Italy
Duration: May 13 2010May 15 2010

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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