Information bounds for nonparametric estimators of L-functionals and survival functionals under censored data

Arnold Janssen () and Andreas Knoch ()

Metrika: International Journal for Theoretical and Applied Statistics, 2016, vol. 79, issue 2, 195-220

Abstract: In the present paper we derive lower asymptotic information bounds of Cramér-Rao type for estimators of nonparametric statistical functionals. The results are based on dense differentiability and dense regularity concepts which lead to weak assumptions. As explicit examples L-estimators are treated. In addition a new rapid method for the treatment of survival functionals under randomly right censored data is presented. For instance, for the famous Kaplan-Meier and Nelson-Aalen estimators, our information bound is just the lower bound obtained earlier in the literature. Copyright Springer-Verlag Berlin Heidelberg 2016

Keywords: Cramér-Rao bound; Nonparametric estimation of functionals; Regularity of estimators; Superefficiency; L-estimator; Kaplan-Meier estimator; Nelson-Aalen estimator; Censored data (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:metrik:v:79:y:2016:i:2:p:195-220

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DOI: 10.1007/s00184-015-0551-y

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