Nonstationary model validation from finite data records

The problems associated with testing a dynamical model, using a data record of finite length, are insufficiency of the data for statistically meaningful decisions, coupling of mean, covariance, and correlation related errors, difficulty of detecting midcourse model departures, and inadequacy of traditional techniques for computing test power for given model alternatives. This paper attempts to provide a comprehensive analysis of nonstationary models via significance tests, specifically addressing these problems. Data records from single and from multiple system operations are analyzed, and the models considered are possibly varying both with respect to time and with respect to operations. Quadratic form distributions prove effective in the statistical analysis.