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Asymptotic properties of maximum likelihood estimators based on progressive Type-II censoring

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Abstract

Hoadley (Ann Math Stat 42:1977–1991, 1971) studied the weak law of large numbers for independent and non-identically distributed random variables. Using that result along with the missing information principle, we establish the consistency and asymptotic normality of maximum likelihood estimators based on progressively Type-II censored samples.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Tamkang University, Tamsui, 25137, Taiwan

    Chien-Tai Lin

  2. Department of Mathematics and Statistics, McMaster University, Hamilto, ON, L8S 4K1, Canada

    N. Balakrishnan

Authors
  1. Chien-Tai Lin
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  2. N. Balakrishnan
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Corresponding author

Correspondence to Chien-Tai Lin.

Additional information

N. Balakrishnan is a visiting professor at King Saud University, Saudi Arabia and National Central University, Taiwan.

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Lin, CT., Balakrishnan, N. Asymptotic properties of maximum likelihood estimators based on progressive Type-II censoring. Metrika 74, 349–360 (2011). https://doi.org/10.1007/s00184-010-0306-8

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  • DOI: https://doi.org/10.1007/s00184-010-0306-8

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