title: A Bayesian model for sparse functional data.
abstract: We propose a method for analyzing data which consist of curves on multiple individuals, i.e., longitudinal or functional data. We use a Bayesian model where curves are expressed as linear combinations of B-splines with random coefficients. The curves are estimated as posterior means obtained via Markov chain Monte Carlo (MCMC) methods, which automatically select the local level of smoothing. The method is applicable to situations where curves are sampled sparsely and/or at irregular time points. We construct posterior credible intervals for the mean curve and for the individual curves. This methodology provides unified, efficient, and flexible means for smoothing functional data.
authors.affiliation: Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA. wesleyt@stat.pitt.edu
authors.forename: Wesley K
authors.initials: WK
authors.lastname: Thompson
authors.forename: Ori
authors.initials: O
authors.lastname: Rosen
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D015331:Cohort Studies; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008137:Longitudinal Studies; D015233:Models, Statistical