Parameter estimation for computationally intensive nonlinear regression with an application to climate modeling

AMS Citation:
Drignei, D., C. E. Forest, and D. W. Nychka, 2008: Parameter estimation for computationally intensive nonlinear regression with an application to climate modeling. Annals of Applied Statistics, 2, 1217-1230, doi:10.1214/08-AOAS210.
Date:2008-12-01
Resource Type:article
Title:Parameter estimation for computationally intensive nonlinear regression with an application to climate modeling
Abstract: 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.
Subject(s):Equilibrium climate sensitivity, Observed and modeled climate, Space–time modeling, statistical surrogate, Temperature data
Peer Review:Refereed
Copyright Information:Copyright 2008 Institute of Mathematical Statistics.
OpenSky citable URL: ark:/85065/d7fb54hw
Publisher's Version: 10.1214/08-AOAS210
Author(s):
  • Dorin Drignei - NCAR/UCAR
  • Chris Forest
  • Douglas Nychka - NCAR/UCAR
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