Composite Coefficient of Determination and Its Application in Ultrahigh Dimensional Variable Screening

DOI10.1080/01621459.2018.1514305zbMath1428.62340OpenAlexW2889315607MaRDI QIDQ5208077

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Publication date: 15 January 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2018.1514305

zbMATH Keywords

kernel regressionvariable screeningbinarizationtest of independenceultrahigh dimensionalityself-equitability

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (8)

On Sure Screening with Multiple Responses ⋮ Model-free, monotone invariant and computationally efficient feature screening with data-adaptive threshold ⋮ Projection divergence in the reproducing kernel Hilbert space: asymptotic normality, block-wise and slicing estimation, and computational efficiency ⋮ Community detection for New York stock market by SCORE-CCD ⋮ Testing unconditional and conditional independence via mutual information ⋮ Sliced Independence Test ⋮ The fused Kolmogorov-Smirnov screening for ultra-high dimensional semi-competing risks data ⋮ Unified mean-variance feature screening for ultrahigh-dimensional regression

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