Dirichlet Process Mixtures of Generalized Linear Models

Lauren Hannah, David Blei, Warren Powell
; Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings 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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v9-hannah10a, title = {Dirichlet Process Mixtures of Generalized Linear Models}, author = {Lauren Hannah and David Blei and Warren Powell}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {313--320}, year = {2010}, editor = {Yee Whye Teh and Mike Titterington}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {JMLR Workshop and Conference Proceedings}, pdf = {http://proceedings.mlr.press/v9/hannah10a/hannah10a.pdf}, url = {http://proceedings.mlr.press/v9/hannah10a.html}, 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.} }
Endnote
%0 Conference Paper %T Dirichlet Process Mixtures of Generalized Linear Models %A Lauren Hannah %A David Blei %A Warren Powell %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-hannah10a %I PMLR %J Proceedings of Machine Learning Research %P 313--320 %U http://proceedings.mlr.press %V 9 %W PMLR %X 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.
RIS
TY - CPAPER TI - Dirichlet Process Mixtures of Generalized Linear Models AU - Lauren Hannah AU - David Blei AU - Warren Powell BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics PY - 2010/03/31 DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-hannah10a PB - PMLR SP - 313 DP - PMLR EP - 320 L1 - http://proceedings.mlr.press/v9/hannah10a/hannah10a.pdf UR - http://proceedings.mlr.press/v9/hannah10a.html AB - 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. ER -
APA
Hannah, L., Blei, D. & Powell, W.. (2010). Dirichlet Process Mixtures of Generalized Linear Models. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in PMLR 9:313-320

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