A nonparametric feature screening method for ultrahigh-dimensional missing response

DOI10.1016/j.csda.2019.106828OpenAlexW2970062792WikidataQ127324412 ScholiaQ127324412MaRDI QIDQ2008122

Jinhan Xie, Xiaodong Yan, Xiao-Xia Li, Nian Sheng Tang

Publication date: 22 November 2019

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

Full work available at URL: https://doi.org/10.1016/j.csda.2019.106828

zbMATH Keywords

imputationmissing at randomfeature screeningultrahigh-dimensional datamarginal Spearman rank correlation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items

Nonparametric independence feature screening for ultrahigh-dimensional missing data ⋮ Screening Methods for Linear Errors-in-Variables Models in High Dimensions ⋮ Empirical likelihood in single-index quantile regression with high dimensional and missing observations

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