Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models

Journal of Agricultural, Biological, and Environmental Statistics
By: , and 

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Abstract

We present an approach for estimating physical parameters in nonlinear models that relies on an approximation to the mechanistic model itself for computational efficiency. The proposed methodology is validated and applied in two different modeling scenarios: (a) Simulation and (b) lower trophic level ocean ecosystem model. The approach we develop relies on the ability to predict right singular vectors (resulting from a decomposition of computer model experimental output) based on the computer model input and an experimental set of parameters. Critically, we model the right singular vectors in terms of the model parameters via a nonlinear statistical model. Specifically, we focus our attention on first-order models of these right singular vectors rather than the second-order (covariance) structure.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models
Series title Journal of Agricultural, Biological, and Environmental Statistics
DOI 10.1007/s13253-011-0073-7
Volume 16
Issue 4
Year Published 2011
Language English
Publisher Springer
Publisher location Amsterdam, Netherlands
Contributing office(s) Colorado Cooperative Fish and Wildlife Research Unit
Description 20 p.
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Journal of Agricultural, Biological, and Environmental Statistics
First page 475
Last page 494