Checking the adequacy of the multivariate semiparametric location shift model

DOI10.1016/j.jmva.2004.04.006zbMath1069.62047OpenAlexW2008286740WikidataQ60430072 ScholiaQ60430072MaRDI QIDQ1776869

Norbert Henze, Li Xing Zhu, Bernhard Klar

Publication date: 12 May 2005

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2004.04.006

zbMATH Keywords

BootstrapGoodness-of-fit testEmpirical characteristic functionMultivariate location modelPermutational principle

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15) Nonparametric statistical resampling methods (62G09)

Related Items (7)

The limit distribution of weighted \(L^2\)-goodness-of-fit statistics under fixed alternatives, with applications ⋮ A general Monte Carlo method for multivariate goodness-of-fit testing applied to elliptical families ⋮ On the estimation of the characteristic function in finite populations with applications ⋮ Change-point methods for multivariate time-series: paired vectorial observations ⋮ Fast tests for the two-sample problem based on the empirical characteristic function ⋮ A Non‐parametric <scp>ANOVA</scp>‐type Test for Regression Curves Based on Characteristic Functions ⋮ Fourier–type tests involving martingale difference processes

Cites Work

• The empirical characteristic function and large sample hypothesis testing
• Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates
• A new approach to the BHEP tests for multivariate normality
• Goodness-of-fit tests for a multivariate distribution by the empirical characteristic function
• Extreme smoothing and testing for multivariate normality
• Goodness-of-fit tests for the Cauchy distribution based on the empirical characteristic function
• A class of invariant consistent tests for multivariate normality
• Bootstrap Critical Values for Testing Homogeneity of Covariance Matrices
• Testing hypotheses about covariance matrices using bootstrap methods
• Adaptive Smoothing and Density-Based Tests of Multivariate Normality
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