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95年10月27日(五) 3:30 ~4:30 P.M.

 

演講日期： 95年10月27日(五) 3:30 ~4:30 P.M.
演講者姓名： 王清雲 教授
演講者服務單位：Fred Hutchingson Cancer Research Center, Washington, U.S.A
演講題目：Boosting with Missing Predictors

摘要： Boosting is an important tool in classification methodology.It combines the performance of many weak classifiers to produce a powerful committee, and its validity can be explained by additive modeling and maximum likelihood. The method has very general applications, especially for high dimensional predictors. For example, it can be applied to distinguish cancer samples from healthy control samples by using antibody microarray data. Microarray data are often high dimensional, and many of them are incomplete. One natural idea is to impute a missing variable based on the observed predictors. The problem itself becomes more challenging when the missing data pattern is not monotone. In this paper, we propose an iterative conditional mean imputation method. It can be applied to the situation when a complete-case subset does not even exist. Simulation results indicates that it performs well. We apply the method to a pancreatic cancer study in which serum-protein microarrays are used for classification.