Journal
BIOMETRICS
Volume 64, Issue 1, Pages 54-63Publisher
WILEY
DOI: 10.1111/j.1541-0420.2007.00829.x
Keywords
Bayesian nonparametric smoothing; B-splines; functional data; longitudinal data; mixed models; MCMC
Funding
- NCRR NIH HHS [G12 RR008124, 5G12 RR008124] Funding Source: Medline
- NIMH NIH HHS [K25 MH076981, K25MH076981-01] Funding Source: Medline
Ask authors/readers for more resources
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
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available