CV-TMLE for Nonpathwise Differentiable Target Parameters

  • Mark J. van der Laan
  • Aurélien Bibaut
  • Alexander R. Luedtke
Chapter
Part of the Springer Series in Statistics book series (SSS)

Abstract

TMLE has been developed for the construction of efficient substitution estimators of pathwise differentiable target parameters. Many parameters are nonpathwise differentiable such as a density or regression curves at a single point in a nonparametric model. In these cases one often uses a specific estimator under a specific smoothness assumptions for which it is possible to establish a limit distribution and thereby provide statistical inference. However, such estimators do not adapt to the true unknown smoothness of the data density and, as a consequence, can be easily outperformed by an adaptive estimator that is able to adapt to the underlying true smoothness.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Mark J. van der Laan
    • 1
  • Aurélien Bibaut
    • 2
  • Alexander R. Luedtke
    • 3
  1. 1.Division of Biostatistics and Department of StatisticsUniversity of CaliforniaBerkeleyUSA
  2. 2.Division of BiostatisticsUniversity of CaliforniaBerkeleyUSA
  3. 3.Vaccine and Infectious Disease DivisionFred Hutchinson Cancer Research CenterSeattleUSA

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