Lifetime Data Analysis

, Volume 15, Issue 3, pp 357–378 | Cite as

Partially linear censored quantile regression

  • Tereza Neocleous
  • Stephen Portnoy


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.


Quantile regression Partially linear models B-splines Censored data Unemployment duration 


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of GlasgowGlasgowUK
  2. 2.Department of StatisticsUniversity of IllinoisChampaignUSA

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