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Targeted Learning Using Adaptive Survey Sampling

  • Antoine Chambaz
  • Emilien Joly
  • Xavier Mary
Chapter
Part of the Springer Series in Statistics book series (SSS)

Abstract

Consider the following situation: we wish to build a confidence interval (CI) for a real-valued pathwise differentiable parameter Ψ evaluated at a law P0, ψ0Ψ(P0), from a data set O1, , O N of independent random variables drawn from P0 but, as is often the case nowadays, N is so large that we will not be able to use all data. To overcome this computational hurdle, we decide (a) to select n among N observations randomly with unequal inclusion probabilities and (b) to adapt TMLE from the smaller data set that results from the selection.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.MAP5 (UMR CNRS 8145) Université Paris DescartesParis cedex 06France
  2. 2.Modal’X, Université Paris NanterreNanterreFrance

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