- June 2010
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Model assessment is a fundamental problem in science and engineering and it addresses the question of the validity of a model in the light of empirical evidence. In this paper, we propose a method for the
assessment of dynamic nonlinear models based on empirical and predictive
cumulative distributions of data and the Kolmogorov-Smirnov statistics.
The technique is based on the generation of discrete random variables
that come from a known discrete distribution if the entertained model is
correct. We provide simulation examples that demonstrate the
performance of the proposed method.