Skip Navigation


Biometrika Advance Access originally published online on January 24, 2008
Biometrika 2008 95(1):123-137; doi:10.1093/biomet/asm081
This Article
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
95/1/123    most recent
asm081v2
asm081v1
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Yu, Z.
Right arrow Articles by Lin, X.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2008 Biometrika Trust

Articles

Nonparametric regression using local kernel estimating equations for correlated failure time data

Zhangsheng Yu

Division of Biostatistics, The Ohio State University College of Public Health, Columbus, Ohio 43210, U.S.A. zyu{at}cph.osu.edu

Xihong Lin

Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A. xlin{at}hsph.harvard.edu

Received for publication 1 January 2006. Revision received 1 June 2007.
  Abstract

We study nonparametric regression for correlated failure time data. Kernel estimating equations are used to estimate nonparametric covariate effects. Independent and weighted-kernel estimating equations are studied. The derivative of the nonparametric function is first estimated and the nonparametric function is then estimated by integrating the derivative estimator. We show that the nonparametric kernel estimator is consistent for any arbitrary working correlation matrix and that its asymptotic variance is minimized by assuming working independence. We evaluate the performance of the proposed kernel estimator using simulation studies, and apply the proposed method to the western Kenya parasitaemia data.

Key Words: Asymptotics • Clustered survival data • Marginal model • Sandwich Estimator • Weighted kernel smoothing • Working-independence kernel estimator


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.