[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CKSS.2007.14.1.111

Comparison of Nonparametric Maximum Likelihood and Bayes Estimators of the Survival Function Based on Current Status Data

Kim, Hee-Jeong (Department of Statistics, Chonnam National University)
Kim, Yong-Dai (Department of Statistics, Seoul National University)
Son, Young-Sook (Department of Statistics, Chonnam National University)

Publication Information

Communications for Statistical Applications and Methods / v.14, no.1, 2007 , pp. 111-119 More about this Journal

Abstract

In this paper, we develop a nonparametric Bayesian methodology of estimating an unknown distribution function F at the given survival time with current status data under the assumption of Dirichlet process prior on F. We compare our algorithm with the nonparametric maximum likelihood estimator through application to simulated data and real data.

Keywords

Current status data; Dirichlet process prior; MCMC algorithm; Bayesian estimation; nonparametric maximum likelihood estimation;

Citations & Related Records

Reference

1	Doss, H. (1994). Bayesian nonparametric estimation for incomplete data via successive substitution sampling. The Annals of Statistics, 22, 1763-1786 DOI
2	Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1, 209-230 DOI
3	Ghosh, J. K. and Ramamoorthi. R. V. (2003). Bayesian Nonparametrics. Springer, New York
4	Lawless, J. F. (2003). Statistical Models and Methods for Lifetime Data. 2nd ed, John Wiley & Sons, New York
5	Meeker, W. Q. and Escobar, L. A. (1998). Statistical Methods for Reliability Data. John Wiley & Sons, New York
6	Nelson, W. B. (1982). Applied Life Data Analysis. John Wiley & Sons, New York
7	Sethuraman, J. (1994). A constructive definition of dirichlet priors. Statistica Sinica, 4, 639-650