Asymptotic theory for estimators under random censorship

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

The product limit estimator {Mathematical expression} of an unknown distribution F is represented as a U-statistic plus an error of the order o(1/n). Using this, the maximum likelihood estimator of the specific risk rate in the time interval [0, M], is shown to admit a two term Edgeworth expansion. This risk rate for a specific cause of death is defined as the ratio of the probability of death, due to that particular cause, in the time interval [0, M], to the mean life time of an individual up to that time point M. Similar expansions for the bootstrapped statistics are used to show that the bootstrap distribution, of the studentized estimator of the risk rate, approximates the sampling distribution better than the corresponding normal distribution.

Original languageEnglish (US)
Pages (from-to)275-290
Number of pages16
JournalProbability Theory and Related Fields
Volume90
Issue number2
DOIs
StatePublished - Jun 1 1991

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Random Censorship
Asymptotic Theory
Estimator
Product-limit Estimator
Edgeworth Expansion
Interval
U-statistics
Sampling Distribution
Maximum Likelihood Estimator
Bootstrap
Gaussian distribution
Statistics
Unknown
Asymptotic theory
Censorship
Term

All Science Journal Classification (ASJC) codes

  • Analysis
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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Asymptotic theory for estimators under random censorship. / Babu, G. Jogesh.

In: Probability Theory and Related Fields, Vol. 90, No. 2, 01.06.1991, p. 275-290.

Research output: Contribution to journalArticle

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