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• W2281076069 abstract "High-dimensional data with a group structure of variables arise always in many contemporary statistical modelling problems. Heavy-tailed errors or outliers in the response often exist in these data. We consider robust group selection for partially linear models when the number of covariates can be larger than the sample size. The non-convex penalty function is applied to achieve both goals of variable selection and estimation in the linear part simultaneously, and we use polynomial splines to estimate the nonparametric component. Under regular conditions, we show that the robust estimator enjoys the oracle property. Simulation studies demonstrate the performance of the proposed method with samples of moderate size. The analysis of a real example illustrates that our method works well." @default.
• W2281076069 created "2016-06-24" @default.
• W2281076069 creator A5036732709 @default.
• W2281076069 creator A5084143431 @default.
• W2281076069 date "2015-12-09" @default.
• W2281076069 modified "2023-10-01" @default.
• W2281076069 title "Robust group non-convex estimations for high-dimensional partially linear models" @default.
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• W2281076069 doi "https://doi.org/10.1080/10485252.2015.1112009" @default.
• W2281076069 hasPublicationYear "2015" @default.
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