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• W2217099769 abstract "We propose a novel kernel estimator of the baseline function in a general high-dimensional Cox model, for which we derive non-asymptotic rates of convergence. To construct our estimator, we first estimate the regression parameter in the Cox model via a LASSO procedure. We then plug this estimator into the classical kernel estimator of the baseline function, obtained by smoothing the so-called Breslow estimator of the cumulative baseline function. We propose and study an adaptive procedure for selecting the bandwidth, in the spirit of Goldenshluger and Lepski (2011). We state non-asymptotic oracle inequalities for the final estimator, which leads to a reduction in the rate of convergence when the dimension of the covariates grows." @default.
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• W2217099769 date "2016-06-01" @default.
• W2217099769 modified "2023-10-18" @default.
• W2217099769 title "Adaptive kernel estimation of the baseline function in the Cox model with high-dimensional covariates" @default.
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• W2217099769 doi "https://doi.org/10.1016/j.jmva.2016.03.002" @default.
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