Optimization of the number of components in the mixed model using multi-criteria decision-making

Abstract

The distributions of empirical data are often complex. Such complexity cannot be sufficiently addressed by the individual theoretical statistical distribution function. Furthermore, the selection of the distribution function becomes more complicated when the empirical data present a multi-peak feature. In such a case, the multiple testing criteria and the mixed model must be considered during the selection of an appropriate distribution function. Aiming at this vague challenge, the present paper proposes a novel method for establishing a mixed model that can describe accurately the distribution characteristics of empirical data. Apart from combining the Akike and Bayesian information criteria to define the feasible solutions of the mixed model, this study also utilizes the root mean squared deviation, coefficient of determination, Kolmogorov–Smirnov test statistic, average deviation in cumulative distribution function, and average deviation in probability distribution function as the testing criteria. In addition, a non-linear programming is used to find the weighting factors of each criterion. The multi-criteria decision-making technology is adopted to comprehensively and objectively integrate these testing criteria into a synthetic indicator. Finally, an optimization algorithm is proposed to determine the optimal number of components in the mixed model. The illustrated results of the simulated data and measured signals confirm that this approach can estimate precisely the number of components as well as establish a highly accurate mixed model.