Asymptotic theory of generalized estimating equations based on jack-knife pseudo-observations

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A general asymptotic theory of estimates from estimating functions based on jack-knife pseudo-observations is established by requiring that the underlying estimator can be expressed as a smooth functional of the empirical distribution. Using results in p-variation norms, the theory is applied to important estimators from time-to-event analysis, namely the Kaplan–Meier estimator and the Aalen–Johansen estimator in a competing risks model, and the corresponding estimators of restricted mean survival and cause-specific lifetime lost. Under an assumption of completely independent censorings, this allows for estimating parameters in regression models of survival, cumulative incidences, restricted mean survival, and cause-specific lifetime lost. Considering estimators as functionals and applying results in p-variation norms is apparently an excellent way of studying the asymptotics of such estimators.
Original languageEnglish
JournalAnnals of Statistics
Issue number5
Pages (from-to)1988-2015
Number of pages28
Publication statusPublished - 31 Oct 2017

    Research areas

  • pseudo-values, functional differentiability, von Mises expansion, pseudo-observation method, p-variation, U-statistics

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