Boosting with missing predictors.
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. However, the calculation of imputation for high-dimensional predictors with missing data may be rather tedious. In this paper, we propose 2 conditional mean imputation methods. They can be applied to the situation even when a complete-case subset does not exist. Simulation results indicate that the proposed methods are superior than other naive methods. We apply the methods to a pancreatic cancer study in which serum protein microarrays are used for classification.
Wang
C Y
CY
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109-1024, USA. cywang@fhcrc.org
Feng
Ziding
Z
D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis / Q000097:blood; D001699:Biometry / Q000379:methods*; D002097:C-Reactive Protein / Q000032:analysis / Q000378:metabolism; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D010190:Pancreatic Neoplasms / Q000097:blood / Q000145:classification / Q000175:diagnosis*; D040081:Protein Array Analysis / Q000379:methods*; D012044:Regression Analysis
D014408:Biomarkers, Tumor; D002097:C-Reactive Protein