Significance tests for functional data with complex dependence structure
KAUST Grant NumberKUS-CI-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/599370
MetadataShow full item record
AbstractWe propose an L (2)-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.
CitationStaicu A-M, Lahiri SN, Carroll RJ (2015) Significance tests for functional data with complex dependence structure. Journal of Statistical Planning and Inference 156: 1–13. Available: http://dx.doi.org/10.1016/j.jspi.2014.08.006.
SponsorsStaicu's research was supported by US National Science Foundation grant number DMS 1007466. Lahiri's research was partially supported by National Science Foundation grants DMS 0707139 and DMS 1007703. Carroll's research was supported by a grant from the National Cancer Institute (R37-CA057030). This publication is based in part on the work supported by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).
PubMed Central IDPMC4443904
CollectionsPublications Acknowledging KAUST Support
- Subtyping of children with developmental dyslexia via bootstrap aggregated clustering and the gap statistic: comparison with the double-deficit hypothesis.
- Authors: King WM, Giess SA, Lombardino LJ
- Issue date: 2007 Jan-Feb
- A resampling-based test for two crossing survival curves.
- Authors: Liu T, Ditzhaus M, Xu J
- Issue date: 2020 Jan 8
- Testing approaches for overdispersion in poisson regression versus the generalized poisson model.
- Authors: Yang Z, Hardin JW, Addy CL, Vuong QH
- Issue date: 2007 Aug
- Quasi-Likelihood Techniques in a Logistic Regression Equation for Identifying <i>Simulium damnosum s.l.</i> Larval Habitats Intra-cluster Covariates in Togo.
- Authors: Jacob BG, Novak RJ, Toe L, Sanfo MS, Afriyie AN, Ibrahim MA, Griffith DA, Unnasch TR
- Issue date: 2012
- Power and Sample Size Calculations for Generalized Estimating Equations via Local Asymptotics.
- Authors: Li Z, McKeague IW
- Issue date: 2013 Jan 1