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September, 1990 Inference for a Nonlinear Counting Process Regression Model
Ian W. McKeague, Klaus J. Utikal
Ann. Statist. 18(3): 1172-1187 (September, 1990). DOI: 10.1214/aos/1176347745


Martingale and counting process techniques are applied to the problem of inference for general conditional hazard functions. This problem was first studied by Beran, who introduced a class of estimators for the conditional cumulative hazard and survival functions in the special case of time-independent covariates. Here the covariate can be time dependent; the classical i.i.d. assumptions are relaxed by replacing them with certain asymptotic stability assumptions, and models involving recurrent failures are included. This is done within the framework of a general nonparametric counting process regression model. Important examples of the model include right-censored survival data, semi-Markov processes, an illness-death process with duration dependence, and age-dependent birth and death processes.


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Ian W. McKeague. Klaus J. Utikal. "Inference for a Nonlinear Counting Process Regression Model." Ann. Statist. 18 (3) 1172 - 1187, September, 1990.


Published: September, 1990
First available in Project Euclid: 12 April 2007

zbMATH: 0721.62087
MathSciNet: MR1062704
Digital Object Identifier: 10.1214/aos/1176347745

Primary: 62M09
Secondary: 62G05, 62J02

Rights: Copyright © 1990 Institute of Mathematical Statistics


Vol.18 • No. 3 • September, 1990
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