Evaluation of Karimi criterion in Markov processes

One of the ways that can be exercised in autoregressive model order chosen is to select the order thatreduces the error of prediction. The final prediction error criterion employs this technique in orderselection. Regrettably, this criterion has poor performance in case of finite samples. Karimi 2007 derived acriterion to address this problem. In this research, the Karimi criterion will be evaluated through the use ofsome distributions. These distributions are Discrete Uniform, Cauchy, t and Log normal, in addition toGaussian distribution which is the basis of the Karimi criterion.