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BOUNDARY CROSSING PROBABILITIES FOR THE CUMULATIVE SAMPLE MEAN

Published online by Cambridge University Press:  08 March 2017

Dashi I. Singham
Affiliation:
Operations Research Department, Naval Postgraduate School, Monterey, CA 93943, USA E-mail: dsingham@nps.edu
Michael P. Atkinson
Affiliation:
Operations Research Department, Naval Postgraduate School, Monterey, CA 93943, USA E-mail: mpatkins@nps.edu

Abstract

We develop a new measure of reliability for the mean behavior of a process by calculating the probability the cumulative sample mean will stay within a given distance from the true mean over a period of time. This probability is derived using boundary-crossing properties of Brownian bridges. We derive finite sample results for independent and identically distributed normal data, limiting results for data meeting a functional central limit theorem, and draw parallels to standard normal confidence intervals. We deliver numerical results for i.i.d., dependent, and queueing processes.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2017 

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References

1. Abundo, M. (2002). Some conditional crossing results of Brownian motion over a piecewise-linear boundary. Statistics & Probability Letters 58(2): 131145.CrossRefGoogle Scholar
2. Alexopoulos, C. (2006). A comprehensive review of methods for simulation output analysis. In Perrone, L.P., Wieland, F.P., Liu, J., Lawson, B.G., Nicol, D.M., & Fujimoto, R.M. (eds.), Proceedings of the 2006 Winter Simulation Conference. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc., pp. 168178.CrossRefGoogle Scholar
3. Atkinson, M.P. & Singham, D.I. (2015). Multidimensional hitting time results for Brownian bridges with moving hyperplanar boundaries. Statistics and Probability Letters 100: 8592.CrossRefGoogle Scholar
4. Batur, D., Goldsman, D., & Kim, S.-H. (2009). An improved standardized time series Durbin–Watson variance estimator for steady-state simulation. Operations Research Letters 37(4): 285289.CrossRefGoogle Scholar
5. Billingsley, P. (1968). Convergence of probability measures. New York: Wiley.Google Scholar
6. Doob, J.L. (1949). Heuristic approach to the Kolmogorov–Smirnov theorems. The Annals of Mathematical Statistics 20(3): 393403.CrossRefGoogle Scholar
7. Fu, J.C. & Wu, T.-L. (2010). Linear and nonlinear boundary crossing probabilities for Brownian motion and related processes. Journal of Applied Probability 47(4): 10581071.CrossRefGoogle Scholar
8. Glynn, P.W. & Iglehart, D.L. (1990). Simulation output analysis using standardized time series. Mathematics of Operations Research 15(1): 116.CrossRefGoogle Scholar
9. Goldsman, D., Meketon, M., & Schruben, L. (1990). Properties of standardized time series weighted area variance estimators. Management Science 36(5): 602612.CrossRefGoogle Scholar
10. Karatzas, I. & Shreve, S. (1998). Brownian motion and stochastic calculus. New York, NY: Springer.CrossRefGoogle Scholar
11. Klenke, A. (2013). Probability theory: a comprehensive course. London: Springer Science & Business Media.Google Scholar
12. Lerche, H.R. & Siegmund, D. (1987). Approximate Exit Probabilities for a Brownian Bridge on a Short Time Interval, and Applications. Technical report, Stanford University.Google Scholar
13. Nagaev, S.V. (1970). On the speed of convergence in a boundary problem. I. Theory of Probability & Its Applications 15(2): 163186.CrossRefGoogle Scholar
14. Scheike, T.H. (1992). A boundary-crossing result for Brownian motion. Journal of Applied Probability 29(2): 448453.Google Scholar
15. Schruben, L. (1983). Confidence interval estimation using standardized time series. Operations Research 31(6): 10901108.CrossRefGoogle Scholar
16. Singham, D.I. & Atkinson, M.P. (2015). Construction of Cumulative Mean Bounds for Simulation Output Analysis. Technical Report NPS-OR-15-004, Naval Postgraduate School.Google Scholar
17. Tafazzoli, A. & Wilson, J.R. (2010). Skart: a skewness-and autoregression-adjusted batch-means procedure for simulation analysis. IIE Transactions 43(2): 110128.Google Scholar
18. Whitt, W. (2002). Stochastic-process limits: an introduction to stochastic-process limits and their application to queues. New York, NY: Springer Science & Business Media.Google Scholar