title: Significance tests for functional data with complex dependence structure.
abstract: We propose anL2-norm based global testing procedure for the null hypothesis that multiple group mean functions are equal, for functional data with complex dependence structure. Specifically, we consider the setting of functional data with a multilevel structure of the form groups-clusters or subjects-units, where the unit-level profiles are spatially correlated within the cluster, and the cluster-level data are independent. Orthogonal series expansions are used to approximate the group mean functions and the test statistic is estimated using the basis coefficients. The asymptotic null distribution of the test statistic is developed, under mild regularity conditions. To our knowledge this is the first work that studies hypothesis testing, when data have such complex multilevel functional and spatial structure. Two small-sample alternatives, including a novel block bootstrap for functional data, are proposed, and their performance is examined in simulation studies. The paper concludes with an illustration of a motivating experiment.
authors.affiliation: Department of Statistics, North Carolina State University, United States.
authors.forename: Ana-Maria
authors.initials: AM
authors.lastname: Staicu
authors.affiliation: Department of Statistics, North Carolina State University, United States.
authors.forename: Soumen N
authors.initials: SN
authors.lastname: Lahiri
authors.affiliation: Department of Statistics, Texas A&M University, United States.
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Carroll
keywords: Block bootstrap; Functional data; Group mean testing; Hierarchical modeling; Significance tests; Spatially correlated curves