A recursive formula for the Kaplan–Meier estimator with mean constraints and its application to empirical likelihood

The Kaplan–Meier estimator is very popular in analysis of survival data. However, it is not easy to compute the ‘constrained’ Kaplan–Meier. Current computational method uses expectation-maximization algorithm to achieve this, but can be slow at many situations. In this note we give a recursive compu...

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Veröffentlicht in:Computational statistics Jg. 30; H. 4; S. 1097 - 1109
Hauptverfasser: Zhou, Mai, Yang, Yifan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2015
Springer Nature B.V
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ISSN:0943-4062, 1613-9658
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Abstract The Kaplan–Meier estimator is very popular in analysis of survival data. However, it is not easy to compute the ‘constrained’ Kaplan–Meier. Current computational method uses expectation-maximization algorithm to achieve this, but can be slow at many situations. In this note we give a recursive computational algorithm for the ‘constrained’ Kaplan–Meier estimator. The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data. Speed comparison to the EM based algorithm favours the current procedure.
AbstractList The Kaplan-Meier estimator is very popular in analysis of survival data. However, it is not easy to compute the 'constrained' Kaplan-Meier. Current computational method uses expectation-maximization algorithm to achieve this, but can be slow at many situations. In this note we give a recursive computational algorithm for the 'constrained' Kaplan-Meier estimator. The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data. Speed comparison to the EM based algorithm favours the current procedure.
Author Yang, Yifan
Zhou, Mai
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  organization: Department of Statistics, University of Kentucky
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  fullname: Yang, Yifan
  email: yifan.yang@uky.edu
  organization: Department of Statistics, University of Kentucky
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CitedBy_id crossref_primary_10_1080_03610918_2020_1808681
crossref_primary_10_1002_sim_9105
crossref_primary_10_1002_wics_1400
crossref_primary_10_1002_wics_1599
crossref_primary_10_1080_10543406_2020_1862143
Cites_doi 10.1080/01621459.1958.10501452
10.1093/biomet/69.3.521
10.1201/9781420036152
10.1093/biomet/75.2.237
10.1198/106186005X59270
10.1016/S0378-3758(98)00156-6
10.1080/10629360600890998
10.1016/0167-7152(94)00210-Y
10.1016/j.jmva.2007.02.007
10.1214/aos/1031594729
10.1080/01621459.1975.10480315
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Keywords Empirical likelihood ratio
NPMLE
Right censored data
Wilks test
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Snippet The Kaplan–Meier estimator is very popular in analysis of survival data. However, it is not easy to compute the ‘constrained’ Kaplan–Meier. Current...
The Kaplan-Meier estimator is very popular in analysis of survival data. However, it is not easy to compute the 'constrained' Kaplan-Meier. Current...
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SubjectTerms Algorithms
Computation
Constraints
Economic Theory/Quantitative Economics/Mathematical Methods
Empirical analysis
Estimators
Lagrange multiplier
Mathematical analysis
Mathematics and Statistics
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Random variables
Recursive
Statistics
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Title A recursive formula for the Kaplan–Meier estimator with mean constraints and its application to empirical likelihood
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