Cramér-von Mises and characteristic function tests for the two and \(k\)-sample problems with dependent data

DOI10.1016/j.csda.2011.12.021zbMath1243.62063OpenAlexW2001651879MaRDI QIDQ435016

François Éthier, Jean-François Quessy

Publication date: 16 July 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2011.12.021

zbMATH Keywords

copulasempirical processesmultiplier central limit theorem

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Central limit and other weak theorems (60F05) Characteristic functions; other transforms (60E10)

Related Items (14)

Computationally efficient approximations for independence tests in non-parametric regression ⋮ Two bootstrap strategies for a \(k\)-problem up to location-scale with dependent samples ⋮ Testing equality of a large number of densities under mixing conditions ⋮ Nonparametric probability weighted empirical characteristic function and applications ⋮ A nonparametric test for paired data ⋮ A weighted bootstrap approximation for comparing the error distributions in nonparametric regression ⋮ Change-point methods for multivariate time-series: paired vectorial observations ⋮ Comparing a large number of multivariate distributions ⋮ Fast tests for the two-sample problem based on the empirical characteristic function ⋮ Testing identifying assumptions in nonseparable panel data models ⋮ A two-sample test for the error distribution in nonparametric regression based on the characteristic function ⋮ On some characterizations and multidimensional criteria for testing homogeneity, symmetry and independence ⋮ Fourier–type tests involving martingale difference processes ⋮ Graphical and formal statistical tools for the symmetry of bivariate copulas

Uses Software

• TwoCop

Cites Work

• An introduction to copulas.
• A nonparametric test for similarity of marginals -- with applications to the assessment of population bioequivalence
• Non-parametric \(k\)-sample tests: density functions vs distribution functions
• Testing for equality between two copulas
• Two-sample tests of the equality of two cumulative incidence functions
• A data-adaptive methodology for finding an optimal weighted generalized Mann-Whitney-Wilcoxon statistic
• \(K\)-sample tests based on the likelihood ratio
• A Studentized permutation test for the non-parametric Behrens-Fisher problem
• A test for the two-sample problem based on empirical characteristic functions
• Rates of convergence for U-statistic processes and their bootstrapped versions
• Weak convergence and empirical processes. With applications to statistics
• Tests of symmetry for bivariate copulas
• Multivariate tests-of-fit and uniform confidence bands using a weighted bootstrap
• Introduction to empirical processes and semiparametric inference
• Testing for Bivariate Extreme Dependence Using Kendall's Process
• Empirical Processes with Applications to Statistics
• An omnibus test for the two-sample problem using the empirical characteristic function
• K-Sample Analogues of the Kolmogorov-Smirnov and Cramer-V. Mises Tests
• A Distribution Free Version of the Smirnov Two Sample Test in the $p$-Variate Case
• A kolmogorov-smirnov type test for positive quadrant dependence
• On the Distribution of the Two-Sample Cramer-von Mises Criterion

This page was built for publication: Cramér-von Mises and characteristic function tests for the two and \(k\)-sample problems with dependent data