A note on modeling sparse exponential-family functional response curves.
Non-Gaussian functional data are considered and modeling through functional principal components analysis (FPCA) is discussed. The direct extension of popular FPCA techniques to the generalized case incorrectly uses a marginal mean estimate for a model that has an inherently conditional interpretation, and thus leads to biased estimates of population and subject-level effects. The methods proposed address this shortcoming by using either a two-stage or joint estimation strategy. The performance of all methods is compared numerically in simulations. An application to ambulatory heart rate monitoring is used to further illustrate the distinctions between approaches.
Institute of Applied Stochastics and Operations Research, Clausthal University of Technology, Clausthal-Zellerfeld, Germany.
Jan
J
Gertheiss
Department of Biostatistics, Columbia University, New York, U.S.A.
Jeff
J
Goldsmith
Department of Statistics, North Carolina State University, Raleigh, U.S.A.
Ana-Maria
AM
Staicu
Binomial Data; Functional Principal Components; Longitudinal Data; Mixed Models; Smoothing; Sparse Sampling Design
Biometrics. 1986 Mar;42(1):121-30
3719049
J Comput Graph Stat. 2015 Apr 1;24(2):477-501
26347592
J Am Stat Assoc. 2013 Oct 1;108(504):1216-1229
24497670
Am J Hypertens. 2012 Jun;25(6):664-71
22378035
Biometrics. 2015 Jun;71(2):344-53
25620473
Biometrika. 2008;95(3):601-619
19396364
Biometrics. 2013 Dec;69(4):903-13
24131242
Stat Med. 2013 Dec 30;32(30):5222-40
24114808
Electron J Stat. 2010;4:1022-1054
21743825
Biometrics. 2013 Mar;69(1):41-51
23003003
Ann Appl Stat. 2009 Mar 1;3(1):458-488
20221415
Stat (Int Stat Inst). 2016;5(1):108-118
27182437