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.
Bibliographical noteFunding 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.
- Self-consistency algorithm