Asymptotic normality of multivariate linear rank statistics in the non- i.i.d. case

DOI10.1214/aos/1176344203zbMath0408.62042OpenAlexW2086560953MaRDI QIDQ1258577

Martien C. A. Van Zuijlen, Frits H. Ruymgaart

Publication date: 1978

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1176344203

zbMATH Keywords

Asymptotic NormalityMultivariate Linear Rank StatisticsNon-I.I.D. CaseTesting Against RegressionTesting Independence

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Nonparametric hypothesis testing (62G10) Asymptotic distribution theory in statistics (62E20)

Related Items (16)

Rank statistics under dependent observations and applications to factorial designs ⋮ Weak convergence of the serial linear rank statistic with unbounded scores and regression constants under mixing conditions ⋮ Weak invariance of the multidimensional rank statistic for nonstationary absolutely regular processes ⋮ Weak convergence of multidimensional rank statistics under \(\phi\)-mixing conditions ⋮ Central limit theorems under alternatives for a broad class of nonparametric statistics ⋮ Multivariate extensions of Spearman's rho and related statistics ⋮ Local comparison of linear rank tests, in the Bahadur sense ⋮ Some asymptotic results for a broad class of nonparametric statistics ⋮ Weak invariance of the multidimensional rank statistic with unbounded scores for nonstationary absolutely regular processes ⋮ Multivariate conditional versions of Spearman's rho and related measures of tail dependence ⋮ Theoretical efficiency comparisons of independence tests based on multivariate versions of Spearman's rho ⋮ Nonparametric inference on multivariate versions of Blomqvist's beta and related measures of tail dependence ⋮ Tests of Multivariate Independence for Ordinal Data ⋮ Conditional independence testing via weighted partial copulas ⋮ Conditional empirical copula processes and generalized measures of association ⋮ Measuring large comovements in financial markets

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