Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems
Journal of Optimization Theory and Applications
Modelling extremal events: for insurance and finance
Modelling extremal events: for insurance and finance
Statistical selection of the best system
Proceedings of the 33nd conference on Winter simulation
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Selecting the best system: selecting the best system: theory and methods
Proceedings of the 35th conference on Winter simulation: driving innovation
A large deviations perspective on ordinal optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
Implications of heavy tails on simulation-based ordinal optimization
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
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We consider the problem of selecting the best among several heavy-tailed systems using a large deviations perspective. In contrast to the light-tailed setting studied by Glynn and Juneja (2004), in the heavy-tailed setting, the probability of false selection is characterized by a rate function that does not require as detailed information about the probability distributions of the system's performance. This motivates the question of studying static policies that could potentially provide convenient implementable in heavy-tailed settings. We concentrate in studying sharp large deviations estimates for the probability of false detection which suggest precise optimal allocation policies when the systems have comparable heavy-tails. Additional optimality insights are given for systems with non-comparable tails.