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Stochastic Approximation Boosting for Incomplete Data Problems
Authors:Joseph Sexton  Petter Laake
Affiliation:Institute of Basic Medical Sciences, Department of Biostatistics, Boks 1122 Blindern, 0317 Oslo, Norway
Abstract:Summary Boosting is a powerful approach to fitting regression models. This article describes a boosting algorithm for likelihood‐based estimation with incomplete data. The algorithm combines boosting with a variant of stochastic approximation that uses Markov chain Monte Carlo to deal with the missing data. Applications to fitting generalized linear and additive models with missing covariates are given. The method is applied to the Pima Indians Diabetes Data where over half of the cases contain missing values.
Keywords:Boosting  Generalized additive model  Generalized linear model  Incomplete data  Markov chain Monte Carlo  Stochastic approximation
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