Feature screening of quadratic inference functions for ultrahigh dimensional longitudinal data

This paper is concerned with feature screening for the ultrahigh dimensional additive models with longitudinal data. The proposed method utilizes the quadratic inference functions to construct the marginal screening measurement, which takes the within-subject correlation into consideration and is mo...

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Bibliographic Details
Published inJournal of statistical computation and simulation Vol. 90; no. 14; pp. 2614 - 2630
Main Authors Lai, Peng, Liang, Weijuan, Wang, Fangjian, Zhang, Qingzhao
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 21.09.2020
Taylor & Francis Ltd
Subjects
Computer simulation
Correlation analysis
Covariance matrix
feature screening
Inference
longitudinal data
Monte Carlo simulation
Quadratic inference function
Screening
sure screening property
ultrahigh dimensional data
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Summary:This paper is concerned with feature screening for the ultrahigh dimensional additive models with longitudinal data. The proposed method utilizes the quadratic inference functions to construct the marginal screening measurement, which takes the within-subject correlation into consideration and is more efficient and robust than some parametric model assumptions for the working covariance matrix in each subject or experimental unit. We also show that the proposed method enjoys the sure screening property under some regularity conditions. Monte Carlo simulation studies and a real data application are conducted to examine the performance of the proposed method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2020.1783666