Semiparametric Inference in the Proportional Odds Regression Model
作者:
Song Yang,
RossL. Prentice,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1999)
卷期:
Volume 94,
issue 445
页码: 125-136
ISSN:0162-1459
年代: 1999
DOI:10.1080/01621459.1999.10473829
出版商: Taylor & Francis Group
关键词: Martingale;Proportional odds regression;Semiparametric model;Survival analysis;Weighted empirical process
数据来源: Taylor
摘要:
For fitting the proportional odds regression model with right-censored survival times, we introduce some weighted empirical odds functions. These functions are solutions of some self-consistency equations and have a nice martingale representation. From these functions, several classes of new regression estimators, such as the pseudo–maximum likelihood estimator, martingale residual-based estimators, and minimum distance estimators, are derived. These estimators have desirable properties such as easy computation, asymptotic normality via a martingale analysis, and reliable asymptotic covariance estimation in closed form. Extensive numerical studies show that the minimumL2distance estimators have very good finite-sample behaviors compared to existing methods. Results of some simulation studies and applications to a real dataset are given. The weighted odds function–based approach also provides inference on the baseline odds function and some measures for lack-of-fit analysis.
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