Robust estimation under progressive censoring

Indrani Basak, N. Balakrishnan

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


For progressively censored failure time data, the influence function and the breakdown point of robust M-estimators are derived. The most robust and the optimal robust estimators are also developed. The optimal members within two classes of ψ-functions are characterized. The first optimality result is the censored data analogue of the general optimality result. The second result pertains to a restricted class of ψ-functions. The usefulness of the two classes of ψ-functions is examined and it was found that the breakdown point and efficiency of the restricted class of optimal estimators compare favorably with those of the corresponding general optimal robust estimators. From the computational point of view, the restricted class of optimal ψ-functions are readily obtainable from the general optimal ψ-functions in the uncensored case. A data set illustrates the optimal robust estimators for the parameters of the extreme value distribution.

Original languageEnglish (US)
Pages (from-to)349-376
Number of pages28
JournalComputational Statistics and Data Analysis
Issue number1-2
StatePublished - Oct 28 2003

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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