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 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.
Original language | English (US) |
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Pages (from-to) | 1217-1230 |
Number of pages | 14 |
Journal | Annals of Applied Statistics |
Volume | 2 |
Issue number | 4 |
DOIs | |
State | Published - Dec 2008 |
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modeling and Simulation
- Statistics, Probability and Uncertainty