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About an adaptively weighted Kaplan-Meier estimate

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Abstract

The 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. We use the Kaplan-Meier estimate to let the weights accommodate right-censored data and use them to define the weighted Kaplan-Meier estimate. The proposed estimate is smoother than the usual Kaplan-Meier estimate and converges uniformly in probability to the target distribution. Simulations show that the performances of the weighted Kaplan-Meier estimate on finite samples exceed that of the usual Kaplan-Meier estimate. A case study is also presented.

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Correspondence to Jean-François Plante.

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Plante, JF. About an adaptively weighted Kaplan-Meier estimate. Lifetime Data Anal 15, 295–315 (2009). https://doi.org/10.1007/s10985-009-9120-x

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  • DOI: https://doi.org/10.1007/s10985-009-9120-x

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