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This article has corrections. See corrections.
Asymptotic normality of corrected estimator in Cox proportional hazards model with measurement error
Volume 1, Issue 1 (2014), pp. 13–32
C. Chimisov   A. Kukush  

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https://doi.org/10.15559/vmsta-2014.1.1.3
Pub. online: 27 June 2014      Type: Research Article      Open accessOpen Access

Received
16 March 2014
Revised
29 May 2014
Accepted
5 June 2014
Published
27 June 2014

Abstract

Cox proportional hazards model is considered. In Kukush et al. (2011), Journal of Statistical Research, Vol. 45, No. 2, 77–94 simultaneous estimators $\lambda _{n}(\cdot )$ and $\beta _{n}$ of baseline hazard rate $\lambda (\cdot )$ and regression parameter β are studied. The estimators maximize the objective function that corrects the log-likelihood function for measurement errors and censoring. Parameter sets for $\lambda (\cdot )$ and β are convex compact sets in $C[0,\tau ]$ and ${\mathbb{R}}^{k}$, respectively. In present paper the asymptotic normality for $\beta _{n}$ and linear functionals of $\lambda _{n}(\cdot )$ is shown. The results are valid as well for a model without measurement errors. A way to compute the estimators is discussed based on the fact that $\lambda _{n}(\cdot )$ is a linear spline.

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Keywords
Asymptotic normality of estimators classical measurement error Corrected Maximum Likelihood Estimator Cox proportional hazards model estimator of baseline hazards function

MSC2010
62N02 62N01 62J12

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