Dirichlet Process Mixtures of Generalized Linear Models
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.
Cite
Text
Hannah et al. "Dirichlet Process Mixtures of Generalized Linear Models." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Hannah et al. "Dirichlet Process Mixtures of Generalized Linear Models." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/hannah2010aistats-dirichlet/)BibTeX
@inproceedings{hannah2010aistats-dirichlet,
title = {{Dirichlet Process Mixtures of Generalized Linear Models}},
author = {Hannah, Lauren and Blei, David and Powell, Warren},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {313-320},
volume = {9},
url = {https://mlanthology.org/aistats/2010/hannah2010aistats-dirichlet/}
}