Assessment of Nonlinear Dynamic Models by Kolmogorov-Smirnov Statistics Articles uri icon

publication date

  • June 2010

start page

  • 5069

end page

  • 5079

issue

  • 10

volume

  • 58

international standard serial number (ISSN)

  • 1053-587X

electronic international standard serial number (EISSN)

  • 1941-0476

abstract

  • 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.