文章基本信息

标题：Assessing Classification Bias in Latent Class Analysis: Comparing Resubstitution and Leave-One-Out Methods
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作者：Kroopnick, Marc H. ; Chen, Jinsong ; Choi, Jaehwa 等
期刊名称：Journal of Modern Applied Statistical Methods
出版年度：2010
卷号：9
期号：1
页码：7
出版社：Wayne State University
摘要：This Monte Carlo simulation study assessed the degree of classification success associated with resubstitution methods in latent class analysis (LCA) and compared those results to those of the leaveone- out (L-O-O) method for computing classification success. Specifically, this study considered a latent class model with two classes, dichotomous manifest variables, restricted conditional probabilities for each latent class and relatively small sample sizes. The performance of resubstitution and L-O-O methods on the lambda classification index was assessed by examining the degree of bias.
关键词：Resubstitution methods; multivariate classification; latent class analysis; leave-one-out; lambda classification index