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Activity Number: 104
Type: Invited
Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract - #307310
Title: Ecological Prediction with Nonlinear Multivariate Time-Frequency Functional Data Models
Author(s): Christopher K. Wikle*+ and Wen-Hsi Yang and Scott H. Holan and Mark L. Wildhaber
Companies: University of Missouri and University of Missouri and University of Missouri and U.S. Geological Survey
Keywords: Bayesian ; spectrogram ; stochastic search variable selection ; big data
Abstract:

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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