Partially linear censored quantile regression

Neocleous, T. and Portnoy, S. (2009) Partially linear censored quantile regression. Lifetime Data Analysis, 15(3), pp. 357-378. (doi: 10.1007/s10985-009-9117-5)

Abstract

Censored regression quantile (CRQ) methods provide a powerful and flexible approach to the analysis of censored survival data when standard linear models are felt to be appropriate. In many cases however, greater flexibility is desired to go beyond the usual multiple regression paradigm. One area of common interest is that of partially linear models: one (or more) of the explanatory covariates are assumed to act on the response through a non-linear function. Here the CRQ approach of Portnoy (J Am Stat Assoc 98:1001–1012, 2003) is extended to this partially linear setting. Basic consistency results are presented. A simulation experiment and unemployment example justify the value of the partially linear approach over methods based on the Cox proportional hazards model and on methods not permitting nonlinearity.

• Item Type: Articles
• Additional Information: The original publication is available at www.springerlink.com
• Keywords: Quantile regression, partially linear models, B-splines, censored data, unemployment duration
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Neocleous, Dr Tereza
• Authors: Neocleous, T., and Portnoy, S.
• Subjects: Q Science > QA Mathematics; H Social Sciences > HA Statistics
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Lifetime Data Analysis
• Publisher: Springer
• ISSN: 1380-7870
• ISSN (Online): 1572-9249
• Published Online: 06 May 2009
• Copyright Holders: Copyright © 2009 Springer
• First Published: First published in Lifetime Data Analysis 15(3):357-378
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher