# Consistent estimation in Cox proportional hazards model with measurement errors and unbounded parameter set

## TL;DR

This paper develops a consistent and asymptotically normal estimator for the Cox proportional hazards model with measurement errors, relaxing previous boundedness assumptions on the parameter set.

## Contribution

It introduces a new estimation method for the Cox model with unbounded parameter sets, extending prior work that assumed boundedness.

## Key findings

- Established strong consistency of the estimator
- Proved asymptotic normality of the estimator
- Extended the model to unbounded parameter sets

## Abstract

Cox proportional hazards model with measurement error is investigated. In Kukush et al. (2011) [Journal of Statistical Research 45, 77-94] and Chimisov and Kukush (2014) [Modern Stochastics: Theory and Applications 1, 13-32] asymptotic properties of simultaneous estimator $\lambda_n(\cdot)$, $\beta_n$ were studied for baseline hazard rate $\lambda(\cdot)$ and regression parameter $\beta$, at that the parameter set $\Theta=\Theta_{\lambda}\times \Theta_{\beta}$ was assumed bounded. In the present paper, the set $\Theta_{\lambda}$ is unbounded from above and not separated away from $0$. We construct the estimator in two steps: first we derive a strongly consistent estimator and then modify it to provide its asymptotic normality.