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Ecological prediction with nonlinear multivariate time-frequency functional data models

Journal of Agricultural, Biological, and Environmental Statistics

By:
, , , and
DOI: 10.1007/s13253-013-0142-1

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Abstract

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to 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 the response variable of interest. 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 the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

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Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Ecological prediction with nonlinear multivariate time-frequency functional data models
Series title:
Journal of Agricultural, Biological, and Environmental Statistics
DOI:
10.1007/s13253-013-0142-1
Volume
18
Issue:
3
Year Published:
2013
Language:
English
Publisher:
Springer
Contributing office(s):
Columbia Environmental Research Center
Description:
25 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Journal of Agricultural, Biological, and Environmental Statistics
First page:
450
Last page:
474
Country:
United States
Other Geospatial:
Lower Missouri River