Kernels, Degrees of Freedom, and Power Properties of Quadratic Distance Goodness-of-Fit Tests

DOI10.1080/01621459.2013.836972zbMath1367.62144OpenAlexW2112884738WikidataQ38657242 ScholiaQ38657242MaRDI QIDQ4975359

Bruce G. Lindsay, Surajit Ray, Marianthi Markatou

Publication date: 4 August 2017

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3979448

zbMATH Keywords

big datahigh-dimensional testingmidpower analysisoptimal kernel constructionPearson-normal kernelpower lemma

Mathematics Subject Classification ID

Density estimation (62G07) Nonparametric hypothesis testing (62G10) Hypothesis testing in multivariate analysis (62H15)

Related Items (6)

Statistical Distances and Their Role in Robustness ⋮ Tests for circular symmetry of complex-valued random vectors ⋮ A general Monte Carlo method for multivariate goodness-of-fit testing applied to elliptical families ⋮ CHARACTERIZATIONS OF MULTINORMALITY AND CORRESPONDING TESTS OF FIT, INCLUDING FOR GARCH MODELS ⋮ Asymptotics, finite-sample comparisons and applications for two-sample tests with functional data ⋮ Statistical distances in goodness-of-fit

Cites Work

• Measuring and testing dependence by correlation of distances
• Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests
• Projection pursuit via white noise matrices
• The topography of multivariate normal mixtures
• Quadratic distances on probabilities: A unified foundation
• A computational strategy for doubly smoothed MLE exemplified in the normal mixture model
• Fisher information matrix: A tool for dimension reduction, projection pursuit, independent component analysis, and more
• On the Asymptotic Properties of a Nonparametric<tex>$L_1$</tex>-Test Statistic of Homogeneity
• Local power properties of kernel based goodness of fit tests

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