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Statistics
A Journal of Theoretical and Applied Statistics
Volume 52, 2018 - Issue 6
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Original Articles

Aalen's linear model for doubly censored data

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Pages 1328-1343 | Received 13 Sep 2017, Accepted 07 Aug 2018, Published online: 24 Aug 2018
 

ABSTRACT

Double censoring often occurs in registry studies when left censoring is present in addition to right censoring. In this work, we examine estimation of Aalen's nonparametric regression coefficients based on doubly censored data. We propose two estimation techniques. The first type of estimators, including ordinary least squared (OLS) estimator and weighted least squared (WLS) estimators, are obtained using martingale arguments. The second type of estimator, the maximum likelihood estimator (MLE), is obtained via expectation-maximization (EM) algorithms that treat the survival times of left censored observations as missing. Asymptotic properties, including the uniform consistency and weak convergence, are established for the MLE. Simulation results demonstrate that the MLE is more efficient than the OLS and WLS estimators.

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Disclosure statement

No potential conflict of interest was reported by the authors.

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