LogitBoost with errors-in-variables

Authors:
Joseph Sexton;Petter Laake
Affiliations:
Department of Biostatistics, Institute of Basic Medical Sciences, Boks 1122 Blindern, 0317 Oslo, Norway;Department of Biostatistics, Institute of Basic Medical Sciences, Boks 1122 Blindern, 0317 Oslo, Norway
Venue:
Computational Statistics & Data Analysis
Year:
2008

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Abstract

The logistic regression model is a popular tool for relating a binary outcome to a set of covariates. In many applications, the covariates of this model are measured with error. An approach to nonparametric logistic regression with covariate measurement error is presented. The estimate of the log-odds is formed using boosted regression trees. The algorithm uses gradient boosting to fit the trees, and their coefficients are determined using an estimating equation closely related to the likelihood score function. The method is examined using simulations.