Parameter estimation for computationally intensive nonlinear regression with an application to climate modeling
Nonlinear regression is a useful statistical tool, relating observed data and a nonlinear function of unknown parameters. When the parameter-dependent nonlinear function is computationally intensive, a straightforward regression analysis by maximum likelihood is not feasible. The method presented in this paper proposes to construct a faster running surrogate for such a computationally intensive nonlinear function, and to use it in a related nonlinear statistical model that accounts for the uncertainty associated with this surrogate. A pivotal quantity in the Earth's climate system is the climate sensitivity: the change in global temperature due to doubling of atmospheric CO₂ concentrations. This, along with other climate parameters, are estimated by applying the statistical method developed in this paper, where the computationally intensive nonlinear function is the MIT 2D climate model.
document
https://n2t.org/ark:/85065/d7fb54hw
eng
geoscientificInformation
Text
publication
2016-01-01T00:00:00Z
publication
2008-12-01T00:00:00Z
Copyright 2008 Institute of Mathematical Statistics.
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