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• W2963604437 abstract "We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses." @default.
• W2963604437 created "2019-07-30" @default.
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• W2963604437 date "2013-01-01" @default.
• W2963604437 modified "2023-09-30" @default.
• W2963604437 title "Non-asymptotic oracle inequalities for the high-dimensional cox regression via lasso" @default.
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• W2963604437 doi "https://doi.org/10.5705/ss.2012.240" @default.
• W2963604437 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3916829" @default.
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