Validating a model using statistics

03-Sep-2016 10:28 by 7 Comments

Validating a model using statistics

A major constraint in building large-scale models of species distribution is the availability of data and therefore the deductive approach is adopted.

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The study underlies the importance of continuous validation of prognostic models over time using statistical tools and the timely recalibration of tree models.► We present a new approach for validation using statistical process control.► The adequacy of tree-models depends on their intended use.► Tree-models have insufficient ability to react to a changed environment.Our procedure factors in the degree of redundancy versus novelty of the experiments used for validation as well as the degree to which the model predicts the observations.We illustrate the methodology first with the maturation of quantum mechanics as the arguably best established physics theory and then with several concrete examples drawn from some of our primary scientific interests: a cellular automaton model for earthquakes, a multifractal random walk model for financial time series, an anomalous diffusion model for solar radiation transport in the cloudy atmosphere, and a computational fluid dynamics code for the Richtmyer–Meshkov instability.The two procedures have been tested on a large number of deductive models built to represent the distribution of vertebrate species in Italy.

Results show that both procedures allow the assessment of the calibration properties of the models.However, their prospective predictive performance over the course of time has not been investigated.In this paper we suggest and apply statistical process control methods to monitor over more than 5 years the prospective predictive performance of TM80 , one of the few classification-tree models published in the literature.A given model is progressively validated by the accumulated confirmations of its predictions by repeated experimental and/or observational tests.The validation of models is becoming a major issue as humans are increasingly faced with decisions involving complex tradeoffs in problems with large uncertainties, as for instance in attempts to control the growing anthropogenic burden on the planet (1) within a risk-cost framework (2, 3) based on predictions of models.We propose to formulate the validation of a given model as an iterative construction process that mimics the often implicit process occurring in the minds of scientists.

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