Maximum likelihood for interval censored data: Consistency and computation

Robert Gentleman, Charles J. Geyer

Research output: Contribution to journalArticlepeer-review

123 Scopus citations


SUMMARY: Standard convex optimization techniques are applied to the analysis of interval censored data. These methods provide easily verifiable conditions for the self-consistent estimator proposed by Turnbull (1976) to be a maximum likelihood estimator and for checking whether the maximum likelihood estimate is unique. A sufficient condition is given for the almost sure convergence of the maximum likelihood estimator to the true underlying distribution function.

Original languageEnglish (US)
Pages (from-to)618-623
Number of pages6
Issue number3
StatePublished - Sep 1994

Bibliographical note

Funding Information:
R. Gentleman was supported by a grant from the Natural Sciences and Engineering Research Council of Canada. G. J. Geyer was supported by a postdoctoral fellowship from the National Science Foundation.


  • Convergence
  • Self-consistency algorithm
  • Uniqueness

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