This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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443
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Type:
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Invited
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Date/Time:
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Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #306010 |
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Title:
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Hierarchical Bayesian Models for Predicting Spatially Correlated Curves
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Author(s):
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Bani K. Mallick*+
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Companies:
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Texas A&M University
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Address:
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Departemnt of Statistics, TAMU 3143, College Station, TX, 77843-3143, USA
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Keywords:
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Functional Data Analysis ;
Spatial process ;
Hierarchical Bayesian model ;
Wavelets
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Abstract:
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Functional data analysis has emerged as a new area of statistical research with wide range of application. Functional data typically consists of curves that are ordered measurement on some interval. We propose some novel models based on wavelets for spatially correlated functional data. The proposed models enable one to regularize curves observed over space as to predict curves at unobserved sites. We have compared the performance of these Bayesian models with several priors on the wavelet coefficients using the posterior predictive criterion. The proposed models are employed to analyze a real porosity data set in petroleum engineering.
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