Title : ( Variable selection and structure identification for ultrahigh-dimensional partially linear additive models with application to cardiomyopathy microarray data )
Authors: Mohammad Kazemi , Davood Shahsavani , Mohammad Arashi ,
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Abstract
In this paper, we introduce a two-step procedure, in the context of ultrahigh-dimensional additive models, to identify nonzero and linear 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 to evaluate the numerical performance of the proposed method and analyze a cardiomyopathy microarray data for an illustration. Numerical studies confirm the fine performance of the proposed method for various semiparametric models.
Keywords
, Dimensionality Reduction, Partially Linear Additive Model, Structure Identification, Sure Screening, Variable Selection
@article{paperid:1081525,
author = {Mohammad Kazemi and Davood Shahsavani and Arashi, Mohammad},
title = {Variable selection and structure identification for ultrahigh-dimensional partially linear additive models with application to cardiomyopathy microarray data},
journal = {Statistics, Optimization and Information Computing},
year = {2018},
volume = {6},
number = {3},
month = {August},
issn = {2311-004X},
keywords = {Dimensionality Reduction; Partially Linear Additive Model; Structure Identification; Sure Screening; Variable Selection},
}
%0 Journal Article
%T Variable selection and structure identification for ultrahigh-dimensional partially linear additive models with application to cardiomyopathy microarray data
%A Mohammad Kazemi
%A Davood Shahsavani
%A Arashi, Mohammad
%J Statistics, Optimization and Information Computing
%@ 2311-004X
%D 2018
