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• W2951474569 abstract "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." @default.
• W2951474569 created "2019-06-27" @default.
• W2951474569 creator A5004208926 @default.
• W2951474569 creator A5054833334 @default.
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• W2951474569 date "2009-09-28" @default.
• W2951474569 modified "2023-09-27" @default.
• W2951474569 title "Dirichlet Process Mixtures of Generalized Linear Models" @default.
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• W2951474569 hasPublicationYear "2009" @default.
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