Bootstrap maximum likelihood for quasi-stationary distributions. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Guo, Guangbao; Allison, James; Zhu, Lixing
• Is Part Of: Journal of nonparametric statistics. Volume 31:Issue 1(2019)
• Page Start: 64
• Page End: 87
• Publication Date: 2019-01-02
• Subjects: Block bootstrap -- Markov processes -- maximum likelihood -- parallel bootstrap -- portfolio processes -- quasi-stationary distributions; Nonparametric statistics -- Periodicals; 519.5
• DOI: 10.1080/10485252.2018.1531130
• Languages: English
• ISSNs: 1048-5252

Abstract

ABSTRACT: 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.