The Lq-norm learning for ultrahigh-dimensional survival data: an integrative framework

DOI10.5705/ss.202017.0537zbMath1453.62688OpenAlexW2954320005WikidataQ98189765 ScholiaQ98189765MaRDI QIDQ5134474

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Publication date: 16 November 2020

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

Full work available at URL: https://doi.org/10.5705/ss.202017.0537

zbMATH Keywords

survival dataCramér-von Mises statisticvariable screeningKolmogorov statistic\(L_q\)-norm learning

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Reliability and life testing (62N05)

Related Items (3)

Censored mean variance sure independence screening for ultrahigh dimensional survival data ⋮ Efficient estimation of the maximal association between multiple predictors and a survival outcome ⋮ Rank-Based Greedy Model Averaging for High-Dimensional Survival Data

Uses Software

• uniCox

Cites Work

• Unnamed Item
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• Feature Screening via Distance Correlation Learning
• Independent Screening for Single-Index Hazard rate Models with Ultrahigh Dimensional Features
• Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis
• A $k$-Sample Model in Order Statistics

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