Bivariate censored regression relying on a new estimator of the joint distribution function

Abstract : In this paper we study a class of $M-$estimators in a regression model under bivariate random censoring and provide a set of sufficient conditions that ensure asymptotic $n^{1/2}-$convergence. The cornerstone of our approach is a new estimator of the joint distribution function of the censored lifetimes. A copula approach is used to modelize the dependence structure between the bivariate censoring times. The resulting estimators present the advantage of being easily computable. A simulation study enlighten the finite sample behaviour of this technique.
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Submitted on : Friday, April 8, 2011 - 1:55:00 PM
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Olivier Lopez, Philippe Saint-Pierre. Bivariate censored regression relying on a new estimator of the joint distribution function. 2011. ⟨hal-00584379⟩

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