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Activity Number:
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328
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #308315 |
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Title:
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Bayesian Self-Modeling Warping Regression
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Author(s):
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Donatello Telesca*+ and Lurdes Inoue
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Companies:
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University of Washington and University of Washington
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Address:
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1816 Boylston Ave 202, Seattle, WA, 98122,
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Keywords:
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Hierarchical Models ; Curve Registration ; MCMC ; Microarray Data ; Network Inference
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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. The analysis often proceed by synchronization of the data through curve registration. In this paper, we propose a Bayesian Hierarchical model for curve registration. Our hierarchical model provides a formal account of amplitude and phase variability while borrowing strength from the data across curves in the estimation of the model parameters. We discuss extensions of the model by utilizing penalized B---splines in the representation of the shape and time---transformation functions, and by allowing random image sets in the time transformation. We discuss applications of our model to simulated data as well as to two datasets. In particular, we illustrate using our model in a non--standard analysis aimed at investigating regulatory network in time course microarray data.
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