CFE 2019: Start Registration
View Submission - CMStatistics
B1698
Title: Time-varying feature selection for longitudinal analysis Authors:  Lan Xue - Oregon State University (United States) [presenting]
Abstract: Time-varying coefficient model selection and estimation is proposed 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.