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Assessment of Nonlinear Dynamic Models by Kolmogorov–Smirnov Statistics

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2 Author(s)
Djuric, P.M. ; Dept. of Electr. & Comput. Eng., Stony Brook Univ., Stony Brook, NY, USA ; Miguez, J.

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.

Published in:

Signal Processing, IEEE Transactions on  (Volume:58 ,  Issue: 10 )