Statistical Validation Techniques

TL;DR: The expectation-maximization algorithm is a powerful computational technique for finding the maximum likelihood estimates for parametric models when the data are not fully observed as discussed by the authors, and it can be used to find the parametric model with the highest probability.

TL;DR: In this article , the authors use mathematical modeling, epidemiological expertise, and simulation to design and evaluate two interventions, namely, Cohorting and Queue Splitting, to reduce the waiting time and length of elevator queues.

TL;DR: The proposed method possesses faster and more stable convergence properties when compared with the Monte Carlo expectation–maximization algorithm, and has an accuracy of O p ( 1 / K ) .

TL;DR: A general introduction to validation and verification of simulation models, define the various validation techniques, and present a recommended model validation procedure in this tutorial paper.

TL;DR: The bootstrap method is examined and evaluated as an example of this new generation of statistical tools that take advantage of the high speed digital computer and free the statistician to attack more complicated problems.