Why preferring parametric forecasting to nonparametric methods?

Journal of Theoretical Biology
Franck Jabot

Abstract

A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argume...Continue Reading

References

Apr 20, 2004·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·V F Pisarenko, D Sornette
Nov 15, 2005·Science·Volker GrimmDonald L DeAngelis
Mar 6, 2010·Computational Statistics & Data Analysis·Catherine M Crespi, W John Boscardin
Jun 18, 2011·Ecology Letters·Florian HartigAndreas Huth
Feb 27, 2013·Proceedings of the National Academy of Sciences of the United States of America·Charles T PerrettiGeorge Sugihara
Jul 6, 2013·Trends in Ecology & Evolution·Matthew R EvansTim G Benton
Oct 9, 2013·Proceedings of the National Academy of Sciences of the United States of America·Florian Hartig, Carsten F Dormann
Nov 28, 2013·Proceedings of the National Academy of Sciences of the United States of America·Charles T PerrettiGeorge Sugihara

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