A Bayesian model for sparse functional data |
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Authors: | Thompson Wesley K Rosen Ori |
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Institution: | Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, U.S.A. email:; and Department of Mathematical Sciences, University of Texas at El Paso, El Paso, Texas 79968, U.S.A. |
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Abstract: | Summary. 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. |
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Keywords: | Bayesian nonparametric smoothing B-splines Functional data Longitudinal data Mixed models MCMC |
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