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AVERAGE DERIVATIVES FOR HAZARD FUNCTIONS

Published online by Cambridge University Press:  08 June 2004

Tue Gørgens
Tue Gørgens
Affiliation:
Australian National University
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Abstract

This paper develops semiparametric kernel-based estimators of risk-specific hazard functions for competing risks data. Both discrete and continuous failure times are considered. The maintained assumption is that the hazard function depends on explanatory variables only through an index. In contrast to existing semiparametric estimators, proportional hazards is not assumed. The new estimators are asymptotically normally distributed. The estimator of index coefficients is root-n consistent. The estimator of hazard functions achieves the one-dimensional rate of convergence. Thus the index assumption eliminates the “curse of dimensionality.” The estimators perform well in Monte Carlo experiments.I thank Denise Doiron for stimulating my interest in this research project and Catherine de Fontenay, Hans Christian Kongsted, Lars Muus, seminar participants, and two anonymous referees for comments on an earlier version of the paper. I gratefully acknowledge the hospitality of the University of Aarhus and the University of Copenhagen, where part of this research was undertaken.

Information

Type
Research Article
Information
Econometric Theory , Volume 20 , Issue 3 , June 2004 , pp. 437 - 463
Copyright
© 2004 Cambridge University Press

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