title: A quasi F-test for functional linear models with functional covariates and its application to longitudinal data.
abstract: Functional linear models are useful in analyzing data from designed experiments and observational studies with functional responses, as well as longitudinal data with a large number of repeated measures on each subject. We propose a quasi F-test for functional linear models with functional covariates and outcomes. We develop a numerical procedure and an efficient approximation for computing p-values, and present a simple way to test individual predictors. For illustration, we apply the proposed procedure to a longitudinal depression data set with repeatedly measured methamphetamine use as a predictor. We conduct a simulation study to assess the size and the power of the test.
authors.affiliation: Department of Statistics, University of California, Los Angeles, CA 90095, USA. hqxu@stat.ucla.edu
authors.forename: Hongquan
authors.initials: H
authors.lastname: Xu
authors.forename: Qing
authors.initials: Q
authors.lastname: Shen
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Yang
authors.forename: Steven
authors.initials: S
authors.lastname: Shoptaw
mesh_terms: D001521:Behavior Therapy; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies / Q000379:methods*; D008297:Male; D008694:Methamphetamine / Q000652:urine; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D019966:Substance-Related Disorders / Q000517:prevention & control
chemical_list: D008694:Methamphetamine