Weak Convergence of a Self-Consistent Estimator of the Survival Function with Doubly Censored Data

Double censoring often occurs in collecting lifetime data and accordingly self-consistent estimators are widely employed for estimating the survival functions. In this paper we prove the weak convergence of self-consistent estimators using classical results on the Fredholm integral equation. Also, a...

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Published in:The Annals of statistics 1990-03, Vol.18 (1), p.391-404
Main Author: Chang, Myron N.
Format: Article
Language:English
Subjects:
62G05
62G99
Censored data
Censorship
Differential equations
doubly censored data
Estimators
Exact sciences and technology
Fredholm integral equation
Logical proofs
Mathematics
Nonparametric inference
Perceptron convergence procedure
Probability and statistics
Random sampling
Reliability functions
Sciences and techniques of general use
self-consistent estimation
Statistical estimation
Statistical variance
Statistics
Survival function
weak convergence
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description Double censoring often occurs in collecting lifetime data and accordingly self-consistent estimators are widely employed for estimating the survival functions. In this paper we prove the weak convergence of self-consistent estimators using classical results on the Fredholm integral equation. Also, a method of calculating the asymptotic variance is presented.
doi_str_mv 10.1214/aos/1176347506
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ispartof The Annals of statistics, 1990-03, Vol.18 (1), p.391-404
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source Project Euclid Complete; JSTOR
subjects 62G05
62G99
Censored data
Censorship
Differential equations
doubly censored data
Estimators
Exact sciences and technology
Fredholm integral equation
Logical proofs
Mathematics
Nonparametric inference
Perceptron convergence procedure
Probability and statistics
Random sampling
Reliability functions
Sciences and techniques of general use
self-consistent estimation
Statistical estimation
Statistical variance
Statistics
Survival function
weak convergence
title Weak Convergence of a Self-Consistent Estimator of the Survival Function with Doubly Censored Data
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