Estimation of the hazard function in a semiparametric model with covariate measurement error
UMR AgroParisTech/INRA MIA 518, Paris, France.
2 URGV UMR INRA 1165/CNRS 8114/UEVE, Évry, France.
3 Université Paris Descartes, Laboratoire MAP5, Paris; email@example.com
Revised: 30 January 2008
We consider a failure hazard function, conditional on a time-independent covariate Z, given by . The baseline hazard function and the relative risk both belong to parametric families with . The covariate Z has an unknown density and is measured with an error through an additive error model U = Z + ε where ε is a random variable, independent from Z, with known density . We observe a n-sample (Xi, Di, Ui), i = 1, ..., n, where Xi is the minimum between the failure time and the censoring time, and Di is the censoring indicator. Using least square criterion and deconvolution methods, we propose a consistent estimator of θ0 using the observations n-sample (Xi, Di, Ui), i = 1, ..., n. We give an upper bound for its risk which depends on the smoothness properties of and as a function of z, and we derive sufficient conditions for the -consistency. We give detailed examples considering various type of relative risks and various types of error density . In particular, in the Cox model and in the excess risk model, the estimator of θ0 is -consistent and asymptotically Gaussian regardless of the form of .
Mathematics Subject Classification: 62G05 / 62F12 / 62G99 / 62J02
Key words: Semiparametric estimation / errors-in-variables model / measurement error / nonparametric estimation / excess risk model / Cox model / censoring / survival analysis / density deconvolution / least square criterion
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