title: Type I error control for tree classification.
abstract: Binary tree classification has been useful for classifying the whole population based on the levels of outcome variable that is associated with chosen predictors. Often we start a classification with a large number of candidate predictors, and each predictor takes a number of different cutoff values. Because of these types of multiplicity, binary tree classification method is subject to severe type I error probability. Nonetheless, there have not been many publications to address this issue. In this paper, we propose a binary tree classification method to control the probability to accept a predictor below certain level, say 5%.
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.
authors.forename: Sin-Ho
authors.initials: SH
authors.lastname: Jung
authors.affiliation: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA.
authors.forename: Yong
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA.
authors.forename: Hongshik
authors.initials: H
authors.lastname: Ahn
keywords: binary tree; classification; permutation; single-step procedure; step-down procedure; type I error