文章基本信息

标题：A Comparison of Two Linear Discriminant Analysis Methods That Use Block Monotone Missing Training Data
本地全文：下载
作者：Phil D. Young ; Dean M. Young ; Songthip T. Ounpraseuth 等
期刊名称：Open Journal of Statistics
印刷版ISSN：2161-718X
电子版ISSN：2161-7198
出版年度：2016
卷号：06
期号：01
页码：172-185
DOI：10.4236/ojs.2016.61015
语种：English
出版社：Scientific Research Publishing
摘要：We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classifying unlabeled multivariate normal observations with equal covariance matrices into one of two classes. Both classes have matching block monotone missing training data. Here, we demonstrate that for intra-class covariance structures with at least small correlation among the variables with missing data and the variables without block missing data, the maximum likelihood estimation substitution classifier outperforms the Chung and Han (2000) classifier regardless of the percent of missing observations. Specifically, we examine the differences in the estimated expected error rates for these classifiers using a Monte Carlo simulation, and we compare the two classifiers using two real data sets with monotone missing data via parametric bootstrap simulations. Our results contradict the conclusions of Chung and Han (2000) that their linear combination classifier is superior to the MLE classifier for block monotone missing multivariate normal data.
关键词：Linear Discriminant Analysis;Monte Carlo Simulation;Maximum Likelihood Estimator;Expected Error Rate;Conditional Error Rate