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Extensions of the kaplan-meier estimator
Communications in Statistics - Simulation and Computation, 1995The Kaplan–Meier estimation (KME) (1958) is a popular nonparametric method in analyzing the survival data. Efron (1967) proposes a re-distribution-to-the-right algorithm for right censored data, which can also be re-distributed from right to left by a method of Dinse (1985).
Wei-Ting Kary Chien, Way Kuo
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A Bias-Corrected Kaplan-Meier Estimator
2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), 2020The Kaplan-Meier estimator (KME) is a classical non-parametric reliability estimator for incomplete data; and it underestimates the reliability. Few estimators have been developed to correct its bias. This paper aims to fill this gap by proposing a bias-corrected estimator.
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Counter-intuitive properties of the Kaplan–Meier estimator
Statistics & Probability Letters, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nishikawa, Masako, Tango, Toshiro
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The multiple imputations based Kaplan–Meier estimator
Statistics & Probability Letters, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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An extended Kaplan-Meier estimator and its applications
Statistics in Medicine, 1998We develop an extension of the Kaplan-Meier estimator for the case of multiple live states. The method can be used to construct prognostic charts for tracking individuals initially in a given condition. It is also the key component in constructing a longitudinal version of the multistate life table.
D, Strauss, R, Shavelle
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About an adaptively weighted Kaplan-Meier estimate
Lifetime Data Analysis, 2009The minimum averaged mean squared error nonparametric adaptive weights use data from m possibly different populations to infer about one population of interest. The definition of these weights is based on the properties of the empirical distribution function.
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A Note on Pooling Kaplan-Meier Estimators
Biometrics, 1993SUMMARY Suppose we have several independent samples of censored data, with possibly different censoring patterns for each sample but a common lifetime distribution. Here we examine the problem of how to efficiently combine the information from all the samples to form an estimator of the common survival function.
Cidambi Srinivasan, Mai Zhou
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Discrepancy with respect to kaplan-meier estimator
Communications in Statistics - Simulation and Computation, 1998As first step of applying the Number-theoretic method to Survival Analysis, we suggest the discrepancy associated with Kaplan-Meier estimator. The convergence rate is still O(n-1:) in some sense. Further studies are given and the simulation results are reported.
Kai-Tai Fang, Zukang Zheng, Wenliang Lu
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Presmoothed Kaplan–Meier and Nelson–Aalen estimators
Journal of Nonparametric Statistics, 2005In this paper a modiÞcation of the Kaplan-Meier and Nelson-Aalen estimators in the right random censorship model is studied. The new estimators are obtained by replacing the censoring indicator variables in the classical deÞnitions by values of a nonparametric regression estimator. Asymptotic normality is obtained and it is shown that this presmoothing
Ricardo Cao +3 more
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On an exponential bound for the Kaplan–Meier estimator
Lifetime Data Analysis, 2007We review limit theory and inequalities for the Kaplan-Meier Kaplan and Meier (J Am Stat Assoc 53:457-481, 1958) product limit estimator of a survival function on the whole line [Formula: see text] . Along the way we provide bounds for the constant in an interesting inequality due to Biotouzé et al.
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