Optimal mean-squared-error batch sizes

Article Abstract:

The minimal mean squared error (mse) approach for batch size selection may be used when batch size sets the parameters of the estimator of the sample mean's variance. A study is conducted to investigate the optimal mse batch size of the following four estimator types: nonoverlapping-batch-means, overlapping-batch-means, standardized-time-series-area, and nonoverlapping-batch-means combined with standardize-time-series-area estimators. The study demonstrates that the data process determines the asymptotic sample mean variance only through its marginal variance and the total of the autocorrelations weighted by their absolute lags. By incorporating these results into the variance findings of D. Goldman and M.S. Meketon (1986), explicit asymptotic approximations are derived for mse, optimal batch size, optimal mse and robustness of the four estimator types.

Methods, Economic lot size, Time-series analysis, Time series analysis

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Strong consistency and other properties of the spectral variance estimator

Article Abstract:

A spectral variance estimator that is strongly consistent justifies the construction of a fixed-width confidence interval. This is useful in steady-state simulation output analysis and is an effective way of deciding how long to sample on a single run of the system. Consistent estimation methods provide advantages for large sample sizes as these methods provide more stable confidence interval levels.

Stochastic processes, Theorems (Mathematics)

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Large-sample results for batch means

Article Abstract:

The techniques for estimating the expected results from a simulation model are presented. The batch-means estimator is a useful tool in forecasting results.

United States, Stationary processes

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