Time‐varying feature selection for longitudinal analysis. (23rd November 2019)

• Record Type: Journal Article
• Authors: Xue, Lan; Shu, Xinxin; Shi, Peibei; Wu, Colin O.; Qu, Annie
• Is Part Of: Statistics in medicine. Volume 39:Number 2(2020)
• Page Start: 156
• Page End: 170
• Publication Date: 2019-11-23
• Subjects: adaptive lasso -- group penalization -- model selection -- polynomial spline -- SCAD -- truncated L1‐penalty; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8412
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : We propose time‐varying coefficient model selection and estimation based on the spline approach, which is capable of capturing time‐dependent covariate effects. The new penalty function utilizes local‐region information for varying‐coefficient estimation, in contrast to the traditional model selection approach focusing on the entire region. The proposed method is extremely useful when the signals associated with relevant predictors are time‐dependent, and detecting relevant covariate effects in the local region is more scientifically relevant than those of the entire region. Our simulation studies indicate that the proposed model selection incorporating local features outperforms the global feature model selection approaches. The proposed method is also illustrated through a longitudinal growth and health study from National Heart, Lung, and Blood Institute.