NDLI: Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models

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Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models

Content Provider	Scilit
Author	Fan, Jianqing Ma, Yunbei Dai, Wei
Copyright Year	2014
Description	The varying-coefficient model is an important class of nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is large, the issue of variable selection arises. In this paper, we propose and investigate marginal nonparametric screening methods to screen variables in sparse ultra-high dimensional varying-coefficient models. The proposed nonparametric independence screening (NIS) selects variables by ranking a measure of the nonparametric marginal contributions of each covariate given the exposure variable. The sure independent screening property is established under some mild technical conditions when the dimensionality is of nonpolynomial order, and the dimensionality reduction of NIS is quantified. To enhance the practical utility and finite sample performance, two data-driven iterative NIS methods are proposed for selecting thresholding parameters and variables: conditional permutation and greedy methods, resulting in Conditional-INIS and Greedy-INIS. The effectiveness and flexibility of the proposed methods are further illustrated by simulation studies and real data applications.
Related Links	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4188418/pdf
Ending Page	1284
Page Count	15
Starting Page	1270
ISSN	01621459
e-ISSN	1537274X
DOI	10.1080/01621459.2013.879828
Journal	Journal of the American Statistical Association
Issue Number	507
Volume Number	109
Language	English
Publisher	Informa UK Limited
Publisher Date	2014-07-03
Access Restriction	Open
Subject Keyword	Statistics and Probability Conditional Permutation False Positive Rates Sparsity Sure Independence Screening Variable Selection
Content Type	Text
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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