Functional modeling approach for discrete scalar outcomes and account for the cross-dependence of multilevel repeated functional observations with Structured Penalties (307905)*Mostafa Zahed, University of Northern Colorado
Keywords: Multilevel Functional data analysis; Mixed models; Multilevel longitudinal data analysis, Intensive longitudinal data, Penalized Functional Regression;
Physical activity during the school day can influence kids to reenergize for being able to concentrate on school performance. Main objective of this study is to explore the impact of daily physical activity on children’s academic performance. Because of the complexity of relationship between physical activity and academic performance it is essential to identify relevant variables such as DIBELS and AIMS. In this study, the intensive functional data is collected longitudinally. Therefore, we present a method for longitudinal functional data analysis with a scalar response and multilevel functional covariates. Hence, the proposed model is a novel mixed functional modeling approach to fit a class of model for discrete scalar outcomes and account for the cross-dependence of multilevel repeated functional observations. As an application, we present analyses of intensive functional longitudinal data collected via accelerometers worn by elementary children with the purpose of assessing associations between daily activity patterns and academic perfor- mance. We show that there is an association between activity patterns and the likelihood of performance on the DIBELS and AIMS.