Adaptive Nonparametric Regression Estimation in Presence of Right Censoring
نویسنده
چکیده
In this paper, we consider the problem of estimating a regression function when the outcome is censored. Two strategies of estimation are proposed: a two-step strategy where the ratio of two projection estimators is used to estimate the regression function; a direct strategy based on a standard mean-square contrast for censored data. For both estimators, non-asymptotic bounds for the integrated mean-square risk are provided and data-driven model selection is performed. In most cases, asymptotically optimal minimax rates of convergence are obtained, when the regression function belongs to a class of Besov functions. August 24, 2006 AMS Classification (2001): 62G08; 62N01.
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