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Stochastic Approximation Boosting for Incomplete Data Problems
Author(s) -
Sexton Joseph,
Laake Petter
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2009.01202.x
Subject(s) - boosting (machine learning) , missing data , covariate , markov chain monte carlo , computer science , markov chain , expectation–maximization algorithm , monte carlo method , mathematics , algorithm , artificial intelligence , machine learning , econometrics , mathematical optimization , statistics , maximum likelihood
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.