Properties of Batched Quadratic-Form Variance Parameter Estimators for Simulations
Published Online:1 May 2001https://doi.org/10.1287/ijoc.13.2.149.10518
References
- Convergence of Probability Measures (1968) (John Wiley and Sons, New York) Google Scholar
- Probability and Measure (1986) 2nd ed.(John Wiley and Sons, New York) Google Scholar
- A Guide to Simulation (1987) 2nd ed.(Springer-Verlag, New York) Crossref, Google Scholar
- The use of subseries for estimating the variance of a general statistic from a stationary sequence. Annals of Statistics (1986) 14:1171–1179Crossref, Google Scholar
- Small sample theory for steady state confidence intervals (1989) (Department of Operations Research, Stanford University, Stanford, CA) . Technical Report No. 37Google Scholar
- Large-sample results for batch means. Management Science (1997) 43:1288–1295Link, Google Scholar
- Strong consistency and other properties of the spectral variance estimator. Management Science (1991) 37:1424–1440Link, Google Scholar
- Mean-square consistency of the variance estimator in steady-state simulation output analysis. Operations Research (1993) 43:282–291Link, Google Scholar
- Strong consistency of the variance estimator in steady-state simulation output analysis. Mathematics of Operations Research (1994) 19:494–512Link, Google Scholar
- Consistency of several variants of the standardized time series area variance estimator. Naval Research Logistics (1995) 42:1161–1176Crossref, Google Scholar
- An implementation of the batch means method. INFORMS Journal on Computing (1997) 9:296–310Link, Google Scholar
- Simulation output analysis using standardized time series. Mathematics of Operations Research (1990) 15:1–16Link, Google Scholar
- Estimating the asymptotic variance with batch means. Operations Research Letters (1991) 10:431–435Crossref, Google Scholar
- Cramér-von Mises variance estimators for simulations. Operations Research (1999) 47:299–309Link, Google Scholar
- A comparison of several variance estimators (1986) (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA) . Technical Report #J-85-12Google Scholar
- New confidence interval estimators using standardized time series. Management Science (1990) 36:393–397Link, Google Scholar
- Statistical Analysis of Stationary Time Series (1957) (John Wiley and Sons, New York) Crossref, Google Scholar
- Simulation Modeling and Analysis (2000) 3rd ed.(McGraw-Hill, New York) Google Scholar
- Overlapping batch means: Something for nothing?. Proceedings of the 1984 Winter Simulation Conference (1984) (Institute of Electrical and Electronics Engineers, Piscataway, NJ) 227–230Google Scholar
- Handbook of the Normal Distribution (1982) (Marcel Dekker, New York) Crossref, Google Scholar
- An Introduction to Probability Theory and Mathematical Statistics (1976) (John Wiley and Sons, New York) Google Scholar
- An investigation of finite-sample behavior of confidence interval estimators. Operations Research (1992) 40:898–913Link, Google Scholar
- Batch size effects in the analysis of simulation output. Operations Research (1982) 30:556–568Link, Google Scholar
- Confidence interval estimation using standardized time series. Operations Research (1983) 31:1090–1108Link, Google Scholar
- Large-sample normality of the batch means variance estimator. Oper. Res. Lett. (2001) . ForthcomingGoogle Scholar
- Variance of the sample mean: Properties and graphs of quadratic-form estimators. Operations Research (1993) 41:501–517Link, Google Scholar
- Optimal mean-squared-error batch sizes. Management Science (1995) 41:110–123Link, Google Scholar
- Improved batching for simulation output analysis, I: Convergence properties (1999a) (Department of Industrial Engineering, North Carolina State University, Raleigh, NC) . Technical ReportGoogle Scholar
- Improved batching for simulation output analysis, II: Algorithm development (1999b) (Department of Industrial Engineering, North Carolina State University, Raleigh, NC) . Technical ReportGoogle Scholar
- Standardized time series Lpp-norm variance estimators for simulations. Management Science (1998) 44:234–245Link, Google Scholar

