Model-Free Feature Screening for Ultrahigh Dimensional Data through a Modified Blum-Kiefer-Rosenblatt Correlation

DOI10.5705/ss.202016.0264zbMath1394.62075OpenAlexW2606385713MaRDI QIDQ4571212

Yeqing Zhou, Li-ping Zhu

Publication date: 6 July 2018

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.5705/ss.202016.0264

zbMATH Keywords

extreme valuesindependence testsure screening propertyfeature screeningranking consistency propertyBlum-Kiefer-Rosenblatt correlation

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Measures of association (correlation, canonical correlation, etc.) (62H20)

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Maximum-type tests for high-dimensional regression coefficients using Wilcoxon scores ⋮ Model-free feature screening for ultrahigh dimensional data via a Pearson chi-square based index ⋮ A modified mean-variance feature-screening procedure for ultrahigh-dimensional discriminant analysis ⋮ Model-free, monotone invariant and computationally efficient feature screening with data-adaptive threshold ⋮ A distribution-free test of independence based on a modified mean variance index ⋮ Structure learning via unstructured kernel-based M-estimation ⋮ Rank-based indices for testing independence between two high-dimensional vectors ⋮ Unnamed Item ⋮ Sliced Independence Test ⋮ Composite Coefficient of Determination and Its Application in Ultrahigh Dimensional Variable Screening ⋮ Test for conditional independence with application to conditional screening ⋮ Unified mean-variance feature screening for ultrahigh-dimensional regression

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