Robust modal estimation and variable selection for single-index varying-coefficient models |
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Authors: | Jing Yang Hu Yang |
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Affiliation: | College of Mathematics and Statistics, Chongqing University, Chongqing, PR China |
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Abstract: | In this article, we present a new efficient iteration estimation approach based on local modal regression for single-index varying-coefficient models. The resulted estimators are shown to be robust with regardless of outliers and error distributions. The asymptotic properties of the estimators are established under some regularity conditions and a practical modified EM algorithm is proposed for the new method. Moreover, to achieve sparse estimator when there exists irrelevant variables in the index parameters, a variable selection procedure based on SCAD penalty is developed to select significant parametric covariates and the well-known oracle properties are also derived. Finally, some numerical examples with various distributed errors and a real data analysis are conducted to illustrate the validity and feasibility of our proposed method. |
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Keywords: | Local modal regression Oracle property Robustness Single-index varying-coefficient model Variable selection |
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