Abstract Details
Activity Number:
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104
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Type:
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Invited
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Date/Time:
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Monday, August 5, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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Abstract - #307310 |
Title:
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Ecological Prediction with Nonlinear Multivariate Time-Frequency Functional Data Models
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Author(s):
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Christopher K. Wikle*+ and Wen-Hsi Yang and Scott H. Holan and Mark L. Wildhaber
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Companies:
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University of Missouri and University of Missouri and University of Missouri and U.S. Geological Survey
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Keywords:
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Bayesian ;
spectrogram ;
stochastic search variable selection ;
big data
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Abstract:
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Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to the improvements in technology, the amount of high frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time- frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to response variables. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate our approach on ecological processes.
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Authors who are presenting talks have a * after their name.
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