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Parameter estimation for computationally intensive nonlinear regression with an application to climate modeling
- Source :
- Annals of Applied Statistics 2008, Vol. 2, No. 4, 1217-1230
- Publication Year :
- 2009
-
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 $\mathrm{CO}_2$ 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.<br />Comment: Published in at http://dx.doi.org/10.1214/08-AOAS210 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
- Subjects :
- Statistics - Applications
Subjects
Details
- Database :
- arXiv
- Journal :
- Annals of Applied Statistics 2008, Vol. 2, No. 4, 1217-1230
- Publication Type :
- Report
- Accession number :
- edsarx.0901.3665
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1214/08-AOAS210