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Activity Number:
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216
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Type:
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Invited
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Date/Time:
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Tuesday, August 5, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #300329 |
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Title:
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Bayesian Curve Classification Using Wavelets
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Author(s):
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Bani K. Mallick*+ and Xiaohui S. Wang and Shubhankar Ray
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Companies:
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Texas A&M University and The University of Texas-Pan American and Merck Research Laboratories
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Address:
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Department of Statistics, TAMU3143, College station, TX, 77845,
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Keywords:
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Functional Data ; Logit Link ; MCMC ; wavelets ; Logistic Classification
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Abstract:
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We propose classification models for binary and multicategory data where the predictor is a random function. We use Bayesian modeling with wavelet basis functions which have nice approximation properties over a large class of functional spaces and can accommodate a variety of functional forms observed in real life applications. We develop an unified hierarchical model to encompass both the adaptive wavelet based function estimation model as well as the logistic classification model. These two models are coupled together to borrow strengths from each other in this unified hierarchical framework. The use of Gibbs sampling with conjugate priors for posterior inference makes the method computationally feasible. We compare the performance of the proposed model with other classification methods such as the existing naive plug-in methods by analyzing simulated and real data sets.
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