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Activity Number:
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360
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #304256 |
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Title:
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Functional Mixed Registration Models
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Author(s):
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Donatello Telesca*+ and Lurdes Inoue
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Companies:
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The University of Texas M.D. Anderson Cancer Center and University of Washington
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Address:
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1515 Holcombe Blvd., Houston, TX, 77030,
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Keywords:
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Curve Registraition ; Functional Data Analysis ; MCMC
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Abstract:
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Functional data often exhibit a common shape but also variations in amplitude and phase across curves. We define a class of probability models, which combine curve registration with functional mixed effects modeling, discriminating phase and amplitude variability in a joint fashion. We discuss this class of models with a focus on penalized smoothing splines and propose Bayesian inferential procedures based on Markov Chain Monte Carlo samples from the posterior distribution of the functions of interest. We illustrate the application of our model using simulated data as well as to two data sets, namely, the Berkeley study on human growth and a study on the pharmacokinetics of the drug Remifentanil. Time permitting, we will introduce a generalized view of curve registration with applications to longitudinal counts of criminal activity.
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