Statistical inference for the extreme value distribution under adaptive Type-II progressive censoring schemes

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Zhi-Sheng Ye
  • Ping-Shing Chan
  • Min Xie
  • Hon Keung Tony Ng


Original languageEnglish
Pages (from-to)1099-1114
Journal / PublicationJournal of Statistical Computation and Simulation
Issue number5
Publication statusPublished - May 2014


Adaptive Type-II progressive censoring schemes have been shown to be useful in striking a balance between statistical estimation efficiency and the time spent on a life-testing experiment. In this article, some general statistical properties of an adaptive Type-II progressive censoring scheme are first investigated. A bias correction procedure is proposed to reduce the bias of the maximum likelihood estimators (MLEs). We then focus on the extreme value distributed lifetimes and derive the Fisher information matrix for the MLEs based on these properties. Four different approaches are proposed to construct confidence intervals for the parameters of the extreme value distribution. Performance of these methods is compared through an extensive Monte Carlo simulation. © 2013 © 2013 Taylor & Francis.

Research Area(s)

  • adaptive type-II progressive censoring, confidence interval, extreme value distribution, information matrix, the bootstrap

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