Dirichlet process mixtures of generalised linear models

author: Lauren A. Hannah, Department of Operations Research and Financial Engineering, Princeton University
published: May 20, 2010,   recorded: May 2010,   views: 4483


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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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Comment1 User332, June 9, 2010 at 9:17 p.m.:

Interesting presentation

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