Regularized logistic models for probabilistic forecasting and diagnostics

Bröcker, J. (2010) Regularized logistic models for probabilistic forecasting and diagnostics. Monthly Weather Review, 138 (2). pp. 592-604. ISSN 0027-0644 doi: 10.1175/2009MWR3126.1

Abstract/Summary

Logistic models are studied as a tool to convert dynamical forecast information (deterministic and ensemble) into probability forecasts. A logistic model is obtained by setting the logarithmic odds ratio equal to a linear combination of the inputs. As with any statistical model, logistic models will suffer from overfitting if the number of inputs is comparable to the number of forecast instances. Computational approaches to avoid overfitting by regularization are discussed, and efficient techniques for model assessment and selection are presented. A logit version of the lasso (originally a linear regression technique), is discussed. In lasso models, less important inputs are identified and the corresponding coefficient is set to zero, providing an efficient and automatic model reduction procedure. For the same reason, lasso models are particularly appealing for diagnostic purposes.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/29155
Item Type	Article
Refereed	Yes
Divisions	No Reading authors. Back catalogue items Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Meteorological Society
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