Skip Navigation

Biometrika 1998 85(3):593-603; doi:10.1093/biomet/85.3.593
© 1998 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
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 KIM, J.
Right arrow Articles by LEE, S.-Y.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Two-sample goodness-of-fit tests for additive risk models with censored observations

JINHEUM KIM and SEUNG-YEOUN LEE

Department of Applied Statistics, University of Suwon Kyonggido, 445-743, South Koreajinhkim{at}stat.suwon.ac.kr
Department of Applied Statistics, Sejong University Seoul, 143-747, South Korealeesy{at}cs.sejong.ac.kr

The additive risk model assumes that the hazard function associated with a set of covariates is the sum of the baseline hazard function and the regression function of covariates. We propose two different test procedures for checking the adequacy of two-sample additive risk models for randomly censored observations. One is based on the martingale residuals and the other on the difference between weighted estimators of the excess risk. The test statistics are shown to be asymptotically normal under appropriate regularity conditions and consistent under any model misspecifications. Finally, two real examples are provided, along with results of a simulation study.

Key Words: Additive risk model • Consistency • Excess risk • Goodness-of-fit test • Martingale residual • Proportional hazards model • Weighted 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.