Sequential Sampling with Economics of Selection Procedures
Published Online:7 Oct 2011https://doi.org/10.1287/mnsc.1110.1425
References
- Optimal stopping problems for Brownian motion. Adv. Appl. Probab. (1970) 2:259–286Crossref, Google Scholar
- Design and Analysis for Statistical Selection, Screening, and Multiple Comparisons (1995) (John Wiley & Sons, New York) Google Scholar
- Bayesian Theory (1994) (Wiley, Chichester, UK) Crossref, Google Scholar
- Stochastic Optimal Control: The Discrete Time Case (1978) (Academic Press, Belmont, MA) Google Scholar
- Selecting a selection procedure. Management Sci. (2007) 53(12):1916–1932Link, Google Scholar
- Optimal learning and experimentation in bandit problems. J. Economic Dynam. Control (2002) 27(1):87–108Crossref, Google Scholar
- Sequential generalized likelihood ratios and adaptive treatment allocation for optimal sequential selection. Sequential Analysis (2006) 25(2):179–201Crossref, Google Scholar
- Sequential tests for the mean of a normal distribution. Proc. Fourth Berkeley Sympos. Math. Statist. Probab. (1961) 1(University of California Press, Berkeley, CA) 79–91Google Scholar
- A Bayes sequential sampling inspection plan. Ann. Math. Statist. (1965) 36(5):1387–1407Crossref, Google Scholar
- , Rossetti M. D., Hill R. R., Johansson B., Dunkin A., Ingalls R. G. The conjunction of the knowledge-gradient and the economic approach to simulation selection. Proc. Winter Simulation Conf. (2009) (IEEE, Piscataway, NJ) 528–539Google Scholar
- An economic analysis of simulation selection problems. Management Sci. (2009) 55(3):421–437Link, Google Scholar
- New two-stage and sequential procedures for selecting the best simulated system. Oper. Res. (2001) 49(5):732–743Link, Google Scholar
- Sequential sampling to myopically maximize the expected value of information. INFORMS J. Comput. (2010) 22(1):71–80Link, Google Scholar
- Optimal Statistical Decisions (1970) (McGraw-Hill, New York) Google Scholar
- , Mason S. J., Hill R. R., Mönch L., Rose O., Jefferson T., Fowler J. W. The knowledge-gradient stopping rule for ranking and selection. Proc. Winter Simulation Conf. (2008) (IEEE, Piscataway, NJ) 305–312Google Scholar
- Paradoxes in learning and the marginal value of information. Decision Anal. (2010) 7(4):378–403Link, Google Scholar
- A knowledge-gradient policy for sequential information collection. SIAM J. Control Optim. (2008) 47(5):2410–2439Crossref, Google Scholar
- Bayesian look ahead one-stage sampling allocations for selecting the best population. J. Statist. Planning Inference (1996) 54:229–244Crossref, Google Scholar
- Multiple Decision Procedures: Theory and Methodology of Selecting and Ranking Populations (1979) (Wiley, New York) Google Scholar
- Monte Carlo Methods (1964) (Methuen, London) Crossref, Google Scholar
- The opportunity cost and OCBA selection procedures in ordinal optimization for a fixed number of alternative systems. IEEE Trans. Systems, Machines, Cybernetics C: Appl. Reviews (2007) 37(5):951–961Crossref, Google Scholar
- , Henderson S. G., Nelson B. L. Selecting the best system. Handbooks in Operations Research and Management Science: Simulation (2006) (Elsevier, Amsterdam) 501–534Crossref, Google Scholar
- Adaptive treatment allocation and the multi-armed bandit problem. Ann. Statist. (1987) 15(3):1091–1114Crossref, Google Scholar
- Simulation Modeling and Analysis (2007) 4th ed.(McGraw-Hill, New York) Google Scholar
- Uncertain Judgements: Eliciting Experts' Probabilities (2006) (John Wiley & Sons, Chichester, UK) Crossref, Google Scholar
- Continuous-Time Stochastic Control and Optimization With Financial Applications (2009) (Springer, Berlin) Crossref, Google Scholar
- Sequential Analysis: Tests and Confidence Intervals (1985) (Springer-Verlag, New York) Crossref, Google Scholar

