Stochastic Approximation Boosting for Incomplete Data Problems
Stochastic Approximation Boosting for Incomplete Data Problems
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DOI：
10.1111/j.1541-0420.2009.01202.x
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发表时间：
2009-12
期刊：
影响因子：
1.9
通讯作者：
Joseph Sexton;P. Laake
中科院分区：
文献类型：
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作者：
Joseph Sexton;P. Laake
总结Boosting是拟合回归模型的一种强大方法。本文描述了不完全数据下基于似然估计的Boosting算法。该算法结合了提升与随机近似的变体，使用马尔可夫链蒙特卡罗来处理丢失的数据。应用于拟合广义线性和添加剂模型与缺失的协变量。该方法适用于皮马印第安人糖尿病数据，其中超过一半的病例包含缺失值。
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.