Rank-based score tests for high-dimensional regression coefficients

DOI10.1214/13-EJS839zbMath1349.62218MaRDI QIDQ364206

Zhaojun Wang, Long Feng, Changliang Zou, Bin Chen

Publication date: 6 September 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1377268991

zbMATH Keywords

asymptotic normalityrank regressionWilcoxon testhigh-dimensional datalarge \(p\)small \(n\)

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Hypothesis testing in multivariate analysis (62H15)

Related Items (12)

Maximum-type tests for high-dimensional regression coefficients using Wilcoxon scores ⋮ Variance-estimation-free test of significant covariates in high-dimensional regression ⋮ Rank-based test for slope homogeneity in high-dimensional panel data models ⋮ Comparison of a large number of regression curves ⋮ Test for high dimensional partially linear models ⋮ Testing the Effects of High-Dimensional Covariates via Aggregating Cumulative Covariances ⋮ A new nonparametric test for high-dimensional regression coefficients ⋮ Tests for high-dimensional single-index models ⋮ Conditional mean and quantile dependence testing in high dimension ⋮ Testing regression coefficients in high-dimensional and sparse settings ⋮ Robust \(U\)-type test for high dimensional regression coefficients using refitted cross-validation variance estimation ⋮ A Bayesian-motivated test for high-dimensional linear regression models with fixed design matrix

Uses Software

• GSA

Cites Work

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• Testing Against a High Dimensional Alternative
• Wilcoxon-type generalized Bayesian information criterion
• Hierarchical testing of variable importance
• Tests of hypotheses based on ranks in the general linear model
• Tests for High-Dimensional Regression Coefficients With Factorial Designs

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