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

Dorin Drignei, Chris E. Forest, Doug Nychka

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

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 languageEnglish (US)
Pages (from-to)1217-1230
Number of pages14
JournalAnnals of Applied Statistics
Volume2
Issue number4
DOIs
StatePublished - Dec 2008

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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