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Pré-Publication, Document De Travail Année : 2024

Robust estimation with latin hypercube sampling: a central limit theorem for Z-estimators

Résumé

Latin hypercube sampling (LHS) is a stratified sampling method widely used in computer experiments. In this work, we extend convergence results on the sample mean with Latin hypercube sampling to the class of Z -estimators, gathering all estimators that can be written as zeros of a sample mean function. In particular, the asymptotic vari- ance of this estimate is obtained. This asymptotic vari- ance is shown to be lower using LHS than using classic independent and identically distributed sampling. A Cen- tral Limit theorem for Z -estimators under LHS is also given.
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Dates et versions

hal-04462415 , version 1 (16-02-2024)

Identifiants

  • HAL Id : hal-04462415 , version 1

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Faouzi Hakimi. Robust estimation with latin hypercube sampling: a central limit theorem for Z-estimators. 2024. ⟨hal-04462415⟩
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