Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/211149
Authors: 
Narisetty, Naveen
Koenker, Roger
Year of Publication: 
2019
Series/Report no.: 
cemmap working paper No. CWP56/19
Abstract: 
A new quantile regression model for survival data is proposed that permits a positive proportion of subjects to become unsusceptible to recurrence of disease following treatment or based on other observable characteristics. In contrast to prior proposals for quantile regression estimation of censored survival models, we propose a new "data augmentation" approach to estimation. Our approach has computational advantages over earlier approaches proposed by Wu and Yin (2013, 2017). We compare our method with the two estimation strategies proposed by Wu and Yin and demonstrate its advantageous empirical performance in simulations. The methods are also illustrated with data from a Lung Cancer survival study.
Subjects: 
Survival data
cure proportion
quantile regression
mixture models
data augmentation
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
323.09 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.