SBatch: A spaced batch means procedure for simulation analysis

Lada,; Wilson, F. F.

doi:10.1109/wsc.2007.4419636

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…conducted an extensive experimental performance evaluation of Skart on a wide range of test processes with characteristics that are typical of large-scale practical applications as well as test processes that were deliberately designed to provide an extreme "stress test" of Skart. The experimental results show that Skart compares favorably with other steady-state simulation analysis procedures-namely, its predecessors WASSP Lada et al, 2007), ASAP3, and SBatch (Lada et al, 2008), as well as sequential versions of LABATCH.2, the procedure of Law and Carson (1979), and the spectral procedure of Heidelberger and Welch (1981). Tafazzoli et al (2011a) also developed N-Skart, a nonsequential version of Skart that works with a fixedsize data set.…”

Section: Overview Of the Skart Proceduresmentioning

confidence: 98%

“…nonoverlapping batch means (NBM) (Fishman, 2001;Steiger and Wilson, 2001); overlapping batch means (OBM) (Meketon and Schmeiser, 1984); spaced batch means (Lada et al, 2008); spectral analysis (Heidelberger and Welch, 1981;Lada et al, 2007); standardized time series (STS) (Schruben, 1983;Alexopoulos et al 2007b); and many other techniques described in Chapter 9 of Law (2015). As explained in Section 2, these techniques typically group a fixed number of observations into nonoverlapping or overlapping batches in an attempt to obtain low-bias, low-variance estimators for σ 2 and valid CI estimators for µ, i.e., CIs that achieve a user-specified coverage probability.…”

Section: Introductionmentioning

confidence: 99%

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SPSTS: A sequential procedure for estimating the steady-state mean using standardized time series

et al. 2016

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This article presents SPSTS, an automated sequential procedure for computing point and confidence-interval (CI) estimators for the steady-state mean of a simulation-generated process subject to user-specified requirements for the CI coverage probability and relative half-length. SPSTS is the first sequential method based on standardized time series (STS) area estimators of the steady-state variance parameter (i.e., the sum of covariances at all lags). Whereas its leading competitors rely on the method of batch means to remove bias due to the initial transient, estimate the variance parameter, and compute the CI, SPSTS relies on the signed areas corresponding to two orthonormal STS area variance estimators for these tasks. In successive stages of SPSTS, standard tests for normality and independence are applied to the signed areas to determine (i) the length of the warm-up period; and (ii) a batch size sufficient to ensure adequate convergence of the associated STS area variance estimators to their limiting chi-squared distributions. SPSTS's performance is compared experimentally with that of recent batch-means methods using selected test problems of varying degrees of difficulty. SPSTS performed comparatively well in terms of its average required sample size as well as the coverage and average half-length of the final CIs. ARTICLE HISTORYKEYWORDS Steady-state simulation; simulation output analysis; method of batch means; method of standardized time series; area variance estimator; nonoverlapping variance estimator; overlapping variance estimator uiie-3995r1-final.tex 1

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Section: Overview Of the Skart Proceduresmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

SPSTS: A sequential procedure for estimating the steady-state mean using standardized time series

et al. 2016

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“…We also use the "Quick-and-Dirty" Autoregressive Variance Estimator (QDARVE), which is based on results of Steiger et al (2005), Lada and Wilson (2007), and , and which is simpler to obtain and easier to automate than the STS variance estimators. If we can find a sufficiently large batch size m such that the nonoverlapping batch means X i (m) : i = 1, .…”

Section: "Quick-and-dirty" Autoregressive Variance Estimator (Qdarve)mentioning

confidence: 99%

“…For additional considerations justifying (16), see p. 77 of Bagshaw and Johnson (1975). Steiger et al (2005), Lada and Wilson (2007), and find that if the batch size m is sufficiently large to ensure that ϕ X(m) ≤ 0.8, then their variance estimators similar to QDARVE are sufficiently stable in practice to yield highly …”

Section: [4]mentioning

confidence: 99%

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A distribution-free tabular CUSUM chart for correlated data with automated variance estimation

Lee

Alexopoulos

Goldsman

et al. 2008

2008 Winter Simulation Conference

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We formulate and evaluate distribution-free statistical process control (SPC) charts for monitoring an autocorrelated process when a training data set is used to estimate the marginal mean and variance of the process as well as its variance parameter (i.e., the sum of covariances at all lags). We adapt variance-estimation techniques from the simulation literature for automated use in DFTC-VE, a distributionfree tabular CUSUM chart for rapidly detecting shifts in the mean of an autocorrelated process. Extensive experimentation shows that our variance-estimation techniques do not seriously degrade the performance of DFTC-VE compared with its performance using exact knowledge of the variance parameter; moreover, the performance of DFTC-VE compares favorably with that of other competing distribution-free SPC charts.

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SBatch: A spaced batch means procedure for steady-state simulation analysis

Lada

Steiger

Wilson

2008

Journal of Simulation

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We discuss SBatch, a simplified procedure for steady-state simulation analysis that is based on spaced batch means, incorporating many advantages of its predecessors ASAP3 and WASSP while avoiding many of their disadvantages. SBatch is a sequential procedure designed to produce a confidence-interval (CI) estimator for the steady-state mean response that satisfies user-specified precision and coverage-probability requirements. First SBatch determines a batch size and an interbatch spacer size such that beyond the initial spacer, the spaced batch means approximately form a stationary first-order autoregressive process whose lag-one correlation does not significantly exceed 0.8. Next SBatch delivers a correlation-adjusted CI based on the sample variance and lag-one correlation of the spaced batch means as well as the grand mean of all the individual observations beyond the initial spacer. In an experimental evaluation on a broad range of test problems, SBatch compared favourably with ASAP3 and WASSP.

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SBatch: A spaced batch means procedure for simulation analysis

Cited by 5 publications

References 12 publications

SPSTS: A sequential procedure for estimating the steady-state mean using standardized time series

SPSTS: A sequential procedure for estimating the steady-state mean using standardized time series

A distribution-free tabular CUSUM chart for correlated data with automated variance estimation

SBatch: A spaced batch means procedure for steady-state simulation analysis

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