JOURNAL ARTICLE

Nonparametric estimation of the bivariate survival function for one modified form of current-status data

Pao‐Sheng ShenJai-Chi Kang

Year: 2011 Journal:   Journal of Statistical Computation and Simulation Vol: 81 (9)Pages: 1199-1209   Publisher: Taylor & Francis

Abstract

In this article, the estimation of the bivariate survival function for one modified form of current-status data is considered. Two types of estimators, which are generalizations of the estimators by Campbell and Földes [G. Campbell and A. Földes, Large sample properties of nonparametric statistical inference, in Nonparametric Statistical Inference, B.V. Gnredenko, M.L. Puri, and I. Vineze, eds., North-Holland, Amsterdam, 1982, pp. 103–122] and Dabrowska [D.M. Dabrowska, Kaplan-Meier estimate on the plane, Ann. Stat. 18 (1988), pp. 1475–1489; D.M. Dabrowska, Kaplan-Meier estimate on the plane: weak convergence, LIL, and the bootstrap, J. Multivariate Anal. 29 (1989), pp. 308–325], are proposed. The consistency of the proposed estimators is established. A simulation study is conducted to investigate the performance of the proposed estimators.

Keywords:
Mathematics Nonparametric statistics Estimator Statistics Bivariate analysis Consistency (knowledge bases) Survival function Econometrics Statistical inference Applied mathematics Discrete mathematics

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Topics

Statistical Methods and Inference
Physical Sciences →  Mathematics →  Statistics and Probability
Bayesian Methods and Mixture Models
Physical Sciences →  Computer Science →  Artificial Intelligence
Statistical Methods and Bayesian Inference
Physical Sciences →  Mathematics →  Statistics and Probability

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