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Variable Selection and Structure Identification in High Dimension for Partial Linear Additive Models
Mohammad Kazemi , Davood Shahsavani , Mohammad Arashi
Abstract:   (439 Views)
In this paper, we introduce a two-step procedure, in the context of high dimensional additive models, to identify nonzero linear and nonlinear components. We first develop a sure independence screening procedure based on the distance correlation between predictors and marginal distribution function of the response variable to reduce the dimensionality of the feature space to a moderate scale. Then a double penalization based procedure is applied to identify nonzero and linear components, simultaneously. We conduct extensive simulation experiments and a real data analysis to evaluate the numerical performance of the proposed method.
Keywords: Dimensionality Reduction, Partial Linear Additive Model, Screening, Structure Identification, Variable Selection.
Full-Text [PDF 8484 kb]   (88 Downloads)    
Type of Study: Research | Subject: Applied Statistics
Received: 2017/12/28 | Accepted: 2018/06/18
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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