文章基本信息

标题：SMM: An R Package for Estimation and Simulation of Discrete-time semi-Markov Models
本地全文：下载
作者：Vlad Stefan Barbu ; Caroline Bérard ; Dominique Cellier 等
期刊名称：R News
印刷版ISSN：1609-3631
出版年度：2018
卷号：10
期号：2
页码：226-247
语种：English
出版社：The R Foundation for Statistical Computing
摘要：Semi-Markov models, independently introduced by Lévy (1954), Smith (1955) and Takacs (1954), are a generalization of the well-known Markov models. For semi-Markov models, sojourn times can be arbitrarily distributed, while sojourn times of Markov models are constrained to be exponentially distributed (in continuous time) or geometrically distributed (in discrete time). The aim of this paper is to present the R package SMM, devoted to the simulation and estimation of discrete time multi-state semi-Markov and Markov models. For the semi-Markov case we have considered: parametric and non-parametric estimation; with and without censoring at the beginning and/or at the end of sample paths; one or several independent sample paths. Several discrete-time distributions are considered for the parametric estimation of sojourn time distributions of semi-Markov chains: Uniform, Geometric, Poisson, Discrete Weibull and Binomial Negative.