A nonparametric measure of independence under a hypothesis of independent components

DOI10.1016/0167-7152(92)90197-DzbMath0770.62039OpenAlexW1982110034MaRDI QIDQ1200738

Bruce E. Wahlen, Murray Rosenblatt

Publication date: 16 January 1993

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(92)90197-d

zbMATH Keywords

asymptotic normalitykernel estimatesdensity estimatestest of independenceregularity assumptionsbivariate kernel density estimatorcentral limit theorem for degenerate \(U\)-statisticscomponents of random two-vectorstensor products of marginals

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20)

Related Items (10)

A Semiparametric Kernel Independence Test With Application to Mutational Signatures ⋮ A Nonparametric Test for Independence Based on Sample Space Partitions ⋮ A new test of independence for bivariate observations ⋮ Testing independence by nonparametric kernel method ⋮ Measures of Dependence and Tests of Independence ⋮ Testing serial independence via density-based measures of divergence ⋮ A smoothed bootstrap test for independence based on mutual information ⋮ Power Assessment of a New Test of Independence ⋮ Recognizing and visualizing departures from independence in bivariate data using local Gaussian correlation ⋮ A consistent nonparametric test for serial independence

Cites Work

• Central limit theorem for integrated square error of multivariate nonparametric density estimators
• Nonparametric regression analysis of longitudinal data
• A quadratic measure of deviation of two-dimensional density estimates and a test of independence
• On some global measures of the deviations of density function estimates
• Boundary modification for kernel regression
• Martingale Central Limit Theorems

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