Discrete Optimization of Statistical Sample Sizes in Simulation by Using the Hierarchical Bootstrap Method

TL;DR

This paper introduces a dynamic programming approach to optimize sample sizes in hierarchical bootstrap methods, reducing variance in system characteristic estimators during simulation.

Contribution

It presents a novel optimization technique for sample sizes in hierarchical bootstrap, improving estimator accuracy in simulation models.

Findings

Reduced variance of estimators achieved

Optimization method demonstrated effectiveness

Applicable to complex simulation hierarchies

Abstract

The Bootstrap method application in simulation supposes that value of random variables are not generated during the simulation process but extracted from available sample populations. In the case of Hierarchical Bootstrap the function of interest is calculated recurrently using the calculation tree. In the present paper we consider the optimization of sample sizes in each vertex of the calculation tree. The dynamic programming method is used for this aim. Proposed method allows to decrease a variance of system characteristic estimators.