Stochastic approximation boosting for incomplete data problems.
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.
Sexton
Joseph
J
Institute of Basic Medical Sciences, Department of Biostatistics, Boks 1122 Blindern, 0317 Oslo, Norway. j.a.sexton@medisin.uio.no
Laake
Petter
P
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003920:Diabetes Mellitus / Q000453:epidemiology; D005260:Female; D006801:Humans; D007198:Indians, North American; D016013:Likelihood Functions; D016015:Logistic Models; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012044:Regression Analysis; D012307:Risk Factors; D013269:Stochastic Processes; D014481:United States / Q000453:epidemiology