标题:Bootstrap maximum likelihood for quasi-stationary distributions
作者:Guo, Guangbao; Allison, James; Zhu, Lixing
作者机构:[Guo, Guangbao] Shandong Univ Technol, Sch Math & Stat, Zibo, Peoples R China.; [Allison, James; Zhu, Lixing] North West Univ, Unit Business Math & 更多
通讯作者:Zhu, LX;Zhu, LX
通讯作者地址:[Zhu, LX]North West Univ, Unit Business Math & Informat, Potchefstroom, South Africa;[Zhu, LX]Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Ko 更多
来源:JOURNAL OF NONPARAMETRIC STATISTICS
出版年:2019
卷:31
期:1
页码:64-87
DOI:10.1080/10485252.2018.1531130
关键词:Block bootstrap; Markov processes; maximum likelihood; parallel; bootstrap; portfolio processes; quasi-stationary distributions
摘要:Quasi-stationary distributions have many applications in diverse research fields. We develop a bootstrap-based maximum likelihood (BML) method to deal with quasi-stationary distributions in statistical inference. To efficiently implement a bootstrap procedure that can handle the dependence among observations and speed up the computation, a novel block bootstrap algorithm is proposed to accommodate parallel bootstrap. In particular, we select a suitable block length for use with the parallel bootstrap. The estimation error is investigated to show its convergence. The proposed BML is shown to be asymptotically unbiased. Some numerical studies are given to examine the performance of the new algorithm. The advantages are evidenced through a comparison with some competitors and some examples are analysed for illustration.
收录类别:SCIE;SSCI
资源类型:期刊论文
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