Estimation and Inference in Nonparametric Cox-models: Time Transformation Methods

Jan Terje Kvaløy and Bo Henry Lindqvist
Additional contact information
Jan Terje Kvaløy: Norwegian University of Science and Technology
Bo Henry Lindqvist: Norwegian University of Science and Technology

Computational Statistics, 2003, vol. 18, issue 2, No 3, 205-221

Abstract: Summary In this paper generalization of the Cox proportional hazards regression model to a completely nonparametric model with an unspecified smooth covariate function is studied. A class of methods for Cox-regression called time transformation methods are defined, and a new method for nonparametric Cox-regression in this class is in particular studied. It turns out that this method enjoys a number of useful properties. Ways of doing inference and model checking in nonparametric Cox-models are also discussed, and a brief overview and comparison of methods for nonparametric Cox-regression is given.

Keywords: Nonparametric hazard regression; Covariate order method; Proportional hazard; Kernel estimation; Local likelihood (search for similar items in EconPapers)
Date: 2003
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s001800300141 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:18:y:2003:i:2:d:10.1007_s001800300141

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s001800300141

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().