Sensitivity to Serial Dependency of Input Processes: A Robust Approach

Published Online:https://doi.org/10.1287/mnsc.2016.2667

References

  • Asmussen S, Glynn PW (2007) Stochastic Simulation: Algorithms and Analysis, Stochastic Modelling and Applied Probability, Vol. 57 (Springer Science & Business Media, New York).CrossrefGoogle Scholar
  • Bedford T, Cooke RM (2002) Vines: A new graphical model for dependent random variables. Ann. Statist. 30(4):1031–1068.CrossrefGoogle Scholar
  • Beirlant J, Dudewicz EJ, Györfi L, Van der Meulen EC (1997) Nonparametric entropy estimation: An overview. Internat. J. Math. Statist. Sci. 6(1):17–39.Google Scholar
  • Ben-Tal A, Den Hertog D, De Waegenaere A, Melenberg B, Rennen G (2013) Robust solutions of optimization problems affected by uncertain probabilities. Management Sci. 59(2):341–357.LinkGoogle Scholar
  • Biller B, Nelson BL (2005) Fitting time-series input processes for simulation. Oper. Res. 53(3):549–559.LinkGoogle Scholar
  • Cario MC, Nelson BL (1996) Autoregressive to anything: Time-series input processes for simulation. Oper. Res. Lett. 19(2):51–58.CrossrefGoogle Scholar
  • Cario MC, Nelson BL (1997) Modeling and generating random vectors with arbitrary marginal distributions and correlation matrix. Technical report, Delphi Packard Electric Systems, Warren, OH.Google Scholar
  • Chan NH, Tran LT (1992) Nonparametric tests for serial dependence. J. Time Ser. Anal. 13(1):19–28.CrossrefGoogle Scholar
  • Cox DR, Reid N (2002) The Theory of the Design of Experiments (Chapman & Hall/CRC Press, Boca Raton, FL).Google Scholar
  • Cramér H (1999) Mathematical Methods of Statistics, Princeton Mathematical Series, Vol. 9 (Princeton University Press, Princeton, NJ).Google Scholar
  • Delage E, Ye Y (2010) Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper. Res. 58(3):595–612.LinkGoogle Scholar
  • Fan J, Yao Q (2003) Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Science & Business Media, New York).CrossrefGoogle Scholar
  • Fillmore PA, Williams JP (1971) On operator ranges. Adv. Math. 7(3):254–281.CrossrefGoogle Scholar
  • Ghosh S, Henderson SG (2002) Chessboard distributions and random vectors with specified marginals and covariance matrix. Oper. Res. 50(5):820–834.LinkGoogle Scholar
  • Glasserman P, Xu X (2013) Robust portfolio control with stochastic factor dynamics. Oper. Res. 61(4):874–893.LinkGoogle Scholar
  • Glasserman P, Xu X (2014) Robust risk measurement and model risk. Quant. Finance 14(1):29–58.CrossrefGoogle Scholar
  • Goh J, Sim M (2010) Distributionally robust optimization and its tractable approximations. Oper. Res. 58(4, Part 1):902–917.LinkGoogle Scholar
  • Granger C, Lin J-L (1994) Using the mutual information coefficient to identify lags in nonlinear models. J. Time Ser. Anal. 15(4):371–384.CrossrefGoogle Scholar
  • Granger CW, Maasoumi E, Racine J (2004) A dependence metric for possibly nonlinear processes. J. Time Ser. Anal. 25(5):649–669.CrossrefGoogle Scholar
  • Hampel FR (1974) The influence curve and its role in robust estimation. J. Amer. Statist. Assoc. 69(346):383–393.CrossrefGoogle Scholar
  • Hampel FR, Ronchetti EM, Rousseeuw PJ, Stahel WA (2011) Robust Statistics: The Approach Based on Influence Functions, Wiley Series in Probability and Statistics, Vol. 114 (John Wiley & Sons, New York).Google Scholar
  • Hansen LP, Sargent TJ (2008) Robustness (Princeton University Press, Princeton, NJ).CrossrefGoogle Scholar
  • Ibrahim R, Regnard N, L’Ecuyer P, Shen H (2012) On the modeling and forecasting of call center arrivals. Laroque C, Himmelspach J, Pasupathy R, Rose O, Uhrmacher AM, eds. Proc. 2012 Winter Simulation Conf. (IEEE, Piscataway, NJ), 256–267.CrossrefGoogle Scholar
  • Iyengar GN (2005) Robust dynamic programming. Math. Oper. Res. 30(2):257–280.LinkGoogle Scholar
  • Krishnamurthy A, Kandasamy K, Poczos B, Wasserman L (2014) Nonparametric estimation of Rényi divergence and friends. Proc. 31st Internat. Conf. Machine Learning (JMLR: W&CP Vol. 32), 919–927.Google Scholar
  • Lam H (2016) Robust sensitivity analysis for stochastic systems. Math. Oper. Res. 41(4):1248–1275.LinkGoogle Scholar
  • Liu H, Wasserman L, Lafferty JD (2012) Exponential concentration for mutual information estimation with application to forests. Pereira F, Burges CJC, Bouttou L, Weinberger KQ, eds. Adv. Neural Inform. Processing Systems (NIPS 2012), Vol. 25 (Curran Associates, Red Hook, NY), 2537–2545.Google Scholar
  • Livny M, Melamed B, Tsiolis AK (1993) The impact of autocorrelation on queuing systems. Management Sci. 39(3):322–339.LinkGoogle Scholar
  • Luenberger DG (1969) Optimization by Vector Space Methods (John Wiley & Sons, New York).Google Scholar
  • Mai J-F, Scherer M (2012) Simulating Copulas: Stochastic Models, Sampling Algorithms, and Applications, Series in Quantitative Finance, Vol. 4 (World Scientific, Singapore).CrossrefGoogle Scholar
  • Melamed B (1991) TES: A class of methods for generating autocorrelated uniform variates. ORSA J. Comput. 3(4):317–329.LinkGoogle Scholar
  • Melamed B, Hill JR, Goldsman D (1992) The TES methodology: Modeling empirical stationary time series. Swain JJ, Goldsman D, Crain RC, Wilson JR, eds. Proc. 24th Winter Simulation Conf. (ACM, New York), 135–144.CrossrefGoogle Scholar
  • Mitchell CR, Paulson AS, Beswick CA (1977) The effect of correlated exponential service times on single server tandem queues. Naval Res. Logist. Quart. 24(1):95–112.CrossrefGoogle Scholar
  • Moon K, Hero A (2014) Multivariate f-divergence estimation with confidence. Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, eds. Adv. Neural Inform. Processing Systems (NIPS 2014), Vol. 27 (Curran Associates, Red Hook, NY),2420–2428.Google Scholar
  • Nguyen X, Wainwright MJ, Jordan MI (2007) Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization. Platt JC, Koller D, Singer Y, Roweis ST, eds. Adv. Neural Inform. Processing Systems (NIPS 2007), Vol. 20 (Curran Associates, Red Hook, NY), 1089–1096.Google Scholar
  • Nilim A, El Ghaoui L (2005) Robust control of Markov decision processes with uncertain transition matrices. Oper. Res. 53(5):780–798.LinkGoogle Scholar
  • Pál D, Póczos B, Szepesvári C (2010) Estimation of Rényi entropy and mutual information based on generalized nearest-neighbor graphs. Lafferty J, Williams C, Shaw-Taylor J, Zemel RS, Culotta A, eds. Adv. Neural Inform. Processing Systems (NIPS 2010), Vol. 23 (Curran Associates, Red Hook, NY), 1849–1857.Google Scholar
  • Pang G, Whitt W (2012a) The impact of dependent service times on large-scale service systems. Manufacturing Service Oper. Management 14(2):262–278.LinkGoogle Scholar
  • Pang G, Whitt W (2012b) Infinite-server queues with batch arrivals and dependent service times. Probab. Engrg. Inform. Sci. 26(02):197–220.CrossrefGoogle Scholar
  • Petersen IR, James MR, Dupuis P (2000) Minimax optimal control of stochastic uncertain systems with relative entropy constraints. IEEE Trans. Automatic Control 45(3):398–412.CrossrefGoogle Scholar
  • Póczos B, Schneider JG (2011) On the estimation of alpha-divergences. Proc. 14th Internat. Conf. Artificial Intelligence Statist. (JMLR: W&CP Vol. 15), 609–617.Google Scholar
  • Póczos B, Schneider JG (2012) Nonparametric estimation of conditional information and divergences. Proc. 15th Internat. Conf. Artificial Intelligence Statist. (JMLR: W&CP Vol. 22), 914–923.Google Scholar
  • Robinson PM (1991) Consistent nonparametric entropy-based testing. Rev. Econom. Stud. 58(3):437–453.CrossrefGoogle Scholar
  • Serfling RJ (2009) Approximation Theorems of Mathematical Statistics, Wiley Series in Probability and Statistics, Vol. 162 (John Wiley & Sons, New York).Google Scholar
  • Van Es B (1992) Estimating functionals related to a density by a class of statistics based on spacings. Scand. J. Statist. 19(1):61–72.Google Scholar
  • Ware PP, Page TW Jr, Nelson BL (1998) Automatic modeling of file system workloads using two-level arrival processes. ACM Trans. Modeling Comput. Simulation 8(3):305–330.CrossrefGoogle Scholar
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