Nonparametric Estimation of Bounded Survival Functions with Censored Observations

Authors: Lee C-I.C.¹; Yan X.¹; Shi N-Z.²

Source: Lifetime Data Analysis, Volume 5, Number 1, March 1999 , pp. 81-90(10)

Publisher: Springer

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Abstract:

Stochastic ordering of survival functions is a useful concept in many areas of statistics, especially in nonparametric and order restricted inferences. In this paper we introduce an algorithm to compute maximum likelihood estimates of survival functions where both upper and lower bounds are given. The algorithm allows censored survival data. In a simulation study, we found that the proposed estimates are more efficient than the unrestricted Kaplan-Meier product limit estimates both with and without censored observations.

Keywords: censored data; Kaplan-Meier product limit estimates; Kuhn-Tucker vectors; order restrictions; stochastic ordering

Language: English

Document Type: Regular paper

Affiliations: 1: Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, Newfoundland, Canada, A1C 5S7 2: Department of Mathematics, Northeast Normal University, Changchun, China

Publication date: 1999-03-01