SEMI-MARKOV MODELS FOR PARTIALLY CENSORED DATA
成果类型：
Article
署名作者：
LAGAKOS, SW; SOMMER, CJ; ZELEN, M
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/65.2.311
发表日期：
1978
页码：
311317
关键词：
摘要：
Nonparametric likelihood methods are developed for the analysis of partially censored data arising from a multistate stochastic process. It is assumed that the underlying process follows a semi-Markov model in which state changes form an embedded Markov chain and sojourn times are independent with distributions depending only on adjoining states. The general likelihood function for a set of partially censored observations is determined and maximized nonparametrically. The resulting nonparametric maximum likelihood estimators of the model unknowns have several attractive properties. Approximate distributional results are derived.