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- W2912103814 abstract "In this paper, we consider the estimation problem for the semiparametric regression model with censored data in which the number of explanatory variables p in the linear part is much larger than sample size n, often denoted as p ≫ n. The purpose of this paper is to study the effects of covariates on a response variable censored on the right by a random censoring variable with an unknown probability distribution. It should be noted that high variance and over-fitting are a major concern in such problems. Ordinary statistical methods for estimation cannot be applied directly to censored and high-dimensional data, and therefore a transformation is required. In the context of this paper, a synthetic data transformation is used for solving the censoring problem. We then apply the LASSO-type double-penalized least squares (DPLS) to achieve sparsity in the parametric component and use smoothing splines to estimate the nonparametric component. A Monte Carlo simulation study is performed to show the performance of the estimators and to analyse the effects of the different censoring levels. A real high-dimensional censored data example is used to illustrate the ideas discussed herein." @default.
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- W2912103814 date "2019-01-28" @default.
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- W2912103814 title "Estimation of semiparametric regression model with right-censored high-dimensional data" @default.
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- W2912103814 doi "https://doi.org/10.1080/00949655.2019.1572757" @default.
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