Simulation validation with sparse random data

Abstract

A simulation validation technique based on Theil's inequality coefficient (TIC) is developed for handling correlated random time-series having only sparse data sets. Specifically, a new TIC estimate containing unbiased system and model component variance estimates is derived. Comparisons with this new estimate for serially correlated data indicate that Theil's original inequality coefficient is quite robust with respect to the assumption of utilizing only independent data points. Initially, Theil's inequality coefficient is related to familiar error variance concepts. Thereafter, the new TIC estimate for sparse correlated data sets is derived, and the corresponding continuous formulas for component variance estimates are identified. A grouping procedure is then used to determine confidence intervals in these sparse data cases. Several examples are provided througout to illustrate the special utility of Theil's inequality coefficient for simulation validation.