On Sufficient Dimension Reduction for Proportional Censorship Model with Covariates
Editor(s)
Kontoghiorghes, E. J. and Lee, J. C.
Abstract
The requirement of constant censoring parameter β in Koziol-Green (KG) model is too restrictive. When covariates are present, the conditional KG model (Veraverbekea and Cadarso-Suárez, 2000) which allows β to be dependent on the covariates is more realistic. In this paper, using sufficient dimension reduction methods, we provide a model-free diagnostic tool to test if β is a function of the covariates. Our method also allows us to conduct a model-free selection of the related covariates. A simulation study and a real data analysis are also included to illustrate our approach.
Recommended Citation
X. M. Wen, "On Sufficient Dimension Reduction for Proportional Censorship Model with Covariates," Computational Statistics and Data Analysis, Research Division of the Federal Reserve Bank of St. Louis, Jan 2010.
The definitive version is available at https://doi.org/10.1016/j.csda.2010.02.022
Department(s)
Mathematics and Statistics
Keywords and Phrases
Conditional Koziol-Green model; Multiple covariates; Proportional hazards model; sufficient dimension reduction
International Standard Serial Number (ISSN)
0167-9473
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2010 Research Division of the Federal Reserve Bank of St. Louis, All rights reserved.
Publication Date
01 Jan 2010
