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 argumentation is illustrated with the simple
theta-logistic model that was previously used by Perretti and collaborators to make their point. It should
convince ecologists to stick to standard parametric approaches, until methods have been developed to
assess the reliability of nonparametric forecasting.
A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametricforecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situationcan arise because of two main reasons: the instability of parametric inference procedures in chaoticsystems which can lead to biased parameter estimates, and the discrepancy between the real systemdynamics 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 thedynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach thatappears so promising? It will be here argued that ecological forecasting based on parametric modelspresents two key comparative advantages over nonparametric approaches. First, the likelihood ofparametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures.Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can beestimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametrictechniques provide forecasts with unknown reliability. This argumentation is illustrated with the simpletheta-logistic model that was previously used by Perretti and collaborators to make their point. It shouldconvince ecologists to stick to standard parametric approaches, until methods have been developed toassess the reliability of nonparametric forecasting.
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