Testing independence in high dimensions
type: Item
instance of: scholarly article
title: Testing independence in high dimensions
zbMATH Open document ID: 0729.62041
DOI: 10.1016/0167-7152(91)90166-O
author: Georg R. Heer
published in: Statistics \& Probability Letters
publication date: 1991-01-01T00:00:00Z
review text: Let \((X_ 0,Y_ 0),...,(X_ n,Y_ n)\) be independent copies of some bivariate random vector (X,Y). Denote with \(f_ n\) and \(f^ 1_ n,f^ 2_ n\) bivariate and marginal kernel density estimators of the joint and marginal densities, respectively, computed from \((X_ i,Y_ i)\), \(1\leq i\leq n.\) A test for independence of X and Y is proposed which is based on a comparison of \(f_ n(X_ 0,Y_ 0)\) and \(f^ 1_ n(X_ 0)f^ 2_ n(Y_ 0)\). The paper also contains some power investigations for certain local alternatives which may be expressed in terms of densities.
reviewed by: Winfried Stute
Mathematics Subject Classification ID: 62G10
Mathematics Subject Classification ID: 62G20
zbMATH DE Number: 4205640
zbMATH Keywords: high-dimensional alternative
zbMATH Keywords: uniformly asymptotically unbiased
zbMATH Keywords: orthogonal series expansion
zbMATH Keywords: increasing dimension
zbMATH Keywords: joint densities
zbMATH Keywords: kernel density estimators
zbMATH Keywords: marginal densities
zbMATH Keywords: test for independence
zbMATH Keywords: power investigations
zbMATH Keywords: local alternatives
MaRDI profile type: Publication
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full work available at URL: 0167-7152(91)90166-o
OpenAlex ID: W2077713686
description: scientific article; zbMATH DE number 4205640