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Ecological Prediction With Nonlinear Multivariate Time-Frequency Functional Data Models

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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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Authors and Affiliations

  1. Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO, 65211, USA

    Wen-Hsi Yang

  2. Department of Statistics, University of Missouri, Columbia, MO, USA

    Christopher K. Wikle & Scott H. Holan

  3. US Geological Survey, Columbia, MO, USA

    Mark L. Wildhaber

Authors
  1. Wen-Hsi Yang
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  2. Christopher K. Wikle
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  3. Scott H. Holan
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  4. Mark L. Wildhaber
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Correspondence to Wen-Hsi Yang.

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Yang, WH., Wikle, C.K., Holan, S.H. et al. Ecological Prediction With Nonlinear Multivariate Time-Frequency Functional Data Models. JABES 18, 450–474 (2013). https://doi.org/10.1007/s13253-013-0142-1

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