Variable Selection and Structure Identification in High Dimension for Partial Linear Additive Models
|
Mohammad Kazemi * , Davood Shahsavani , Mohammad Arashi  |
|
|
Abstract: (8226 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]
(2973 Downloads)
|
Type of Study: Research |
Subject:
Applied Statistics Received: 2017/12/28 | Accepted: 2018/06/18 | Published: 2018/10/17
|
|
|
|