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1. Verfasser: Hakimi, Faouzi
Format: Preprint
Veröffentlicht: 2025
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Online-Zugang:https://arxiv.org/abs/2502.06321
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author Hakimi, Faouzi
author_facet Hakimi, Faouzi
contents Latin hypercube sampling (LHS) is a widely used stratified sampling method in computer experiments. In this work, we extend the existing convergence results for the sample mean under LHS to the broader class of $Z$-estimators, estimators defined as the zeros of a sample mean function. We derive the asymptotic variance of these estimators and demonstrate that it is smaller when using LHS compared to traditional independent and identically distributed (i.i.d.) sampling. Furthermore, we establish a Central Limit Theorem for $Z$-estimators under LHS, providing a theoretical foundation for its improved efficiency.
format Preprint
id arxiv_https___arxiv_org_abs_2502_06321
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Robust estimation with latin hypercube sampling: a central limit theorem for Z-estimators
Hakimi, Faouzi
Statistics Theory
Latin hypercube sampling (LHS) is a widely used stratified sampling method in computer experiments. In this work, we extend the existing convergence results for the sample mean under LHS to the broader class of $Z$-estimators, estimators defined as the zeros of a sample mean function. We derive the asymptotic variance of these estimators and demonstrate that it is smaller when using LHS compared to traditional independent and identically distributed (i.i.d.) sampling. Furthermore, we establish a Central Limit Theorem for $Z$-estimators under LHS, providing a theoretical foundation for its improved efficiency.
title Robust estimation with latin hypercube sampling: a central limit theorem for Z-estimators
topic Statistics Theory
url https://arxiv.org/abs/2502.06321