论文信息 - Interval censored data: A note on the nonparametric maximum likelihood estimator of the distribution function

Interval censored data: A note on the nonparametric maximum likelihood estimator of the distribution function

SUMMARY Gentleman & Geyer (1994) discuss the analysis of interval censored data and present results based on standard convex optimisation theory. Here, this problem is viewed from the perspective of a mixing problem of indicator functions. Using an analogy with the problem of mixture distributions a variety of results are easily derived, including a characterisation theorem for the maximum likelihood estimator and various reliably convergent algorithms. Software for the analysis of mixture distributions can be used to find the nonparametric estimate of the distribution function of the interval-censored survival time. Examples are provided to demonstrate the theory.

[1] J. Wellner,et al. Information Bounds and Nonparametric Maximum Likelihood Estimation , 1992 .

[2] R. Wolfe,et al. A semiparametric model for regression analysis of interval-censored failure time data. , 1985, Biometrics.

[3] B. Lindsay. The Geometry of Mixture Likelihoods: A General Theory , 1983 .

[4] A. F. Smith,et al. Statistical analysis of finite mixture distributions , 1986 .

[5] D. Böhning. Likelihood inference for mixtures: Geometrical and other constructions of monotone step-length algorithms , 1989 .

[6] Bruce G. Lindsay,et al. Computer-assisted analysis of mixtures (C.A.MAN) statistical algorithms , 1992 .

[7] C. Geyer,et al. Maximum likelihood for interval censored data: Consistency and computation , 1994 .

[8] Dankmar Bohning. Convergence of Simar's Algorithm for Finding the Maximum Likelihood Estimate of a Compound Poisson Process , 1982 .

[9] D. Böhning. A review of reliable maximum likelihood algorithms for semiparametric mixture models , 1995 .