Bootstrap maximum likelihood for quasi-stationary distributions

Guangbao Guo, James Allison, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)64-87
Number of pages24
JournalJournal of Nonparametric Statistics
Volume31
Issue number1
DOIs
Publication statusPublished - 2 Jan 2019

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Block bootstrap
  • Markov processes
  • maximum likelihood
  • parallel bootstrap
  • portfolio processes
  • quasi-stationary distributions

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