Counting Process Based Dimension Reduction Methods for Censored Outcomes

DOI10.48550/arXiv.1704.05046zbMath1506.62386arXiv1704.05046OpenAlexW2609495673WikidataQ91871300 ScholiaQ91871300MaRDI QIDQ101643

Ruoqing Zhu, Donglin Zeng, Qiang Sun, Tao Wang, Donglin Zeng, Qiang Sun, Tao Wang, Ruoqing Zhu

Publication date: 17 April 2017

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1704.05046

zbMATH Keywords

survival analysisestimating equationsufficient dimension reductionsemiparametric inferencesliced inverse regression

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Nonparametric regression and quantile regression (62G08) Asymptotic distribution theory in statistics (62E20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Censored data models (62N01) Estimation in survival analysis and censored data (62N02)

Related Items (4)

Penalized generalized empirical likelihood with a diverging number of general estimating equations for censored data ⋮ A new reproducing kernel‐based nonlinear dimension reduction method for survival data ⋮ Double-slicing assisted sufficient dimension reduction for high-dimensional censored data ⋮ orthoDr

Uses Software

• R
• Rcpp
• dr

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