About an adaptively weighted Kaplan-Meier estimate |
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Authors: | Jean-François Plante |
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Institution: | 1. Service d’enseignement des méthodes quantitatives de gestion, HEC Montréal, 3000 chemin de la C?te-Sainte-Catherine, Montréal, ON, H3T 2A7, Canada
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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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Keywords: | Adaptive weights Borrowing strength Kaplan-Meier estimate Nonparametrics Survival analysis Weighted inference |
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