:: Volume 12, Issue 2 (3-2019) ::
J. of Stat. Sci. 2019, 12(2): 485-512 Back to browse issues page
Variable Selection and Structure Identification in High Dimension for Partial Linear Additive Models
Mohammad Kazemi , Davood Shahsavani, Mohammad Arashi
Abstract:   (3673 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 238 kb]   (878 Downloads)    
Type of Study: Research | Subject: Applied Statistics
Received: 2017/12/28 | Accepted: 2018/06/18 | Published: 2018/10/17

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Volume 12, Issue 2 (3-2019) Back to browse issues page