The Song rule outperforms optimal-batch-size variance estimators in simulation output analysis

Wheyming Tina Song

European Journal of Operational Research, 2019, vol. 275, issue 3, 1072-1082

Abstract: Estimating the variance of the sample mean is a fundamental problem in Monte Carlo simulation output analysis. The need is to develop a procedure to estimate this variance with minimum mean-squared-error (mse). One of the commonly used approaches is batch-means estimator (BME) including non-overlapping batch means (NBM) and overlapping batch means (OBM). The research into BMEs has pursued the elusive optimal-batch-size for many years. Another commonly used approach is to linearly combine two BMEs with large batch sizes to ignore estimating the bias constant. This paper demonstrates that such two types of pursuits are not the optimal surrogate for the minimum mse.

Keywords: Simulation; The variance of the sample mean; Implementable Song rule; Smallest-batch-sizes linear combination (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:275:y:2019:i:3:p:1072-1082

DOI: 10.1016/j.ejor.2018.11.059

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