A spectral method for confidence interval generation and run length control in simulations
Communications of the ACM - Special issue on simulation modeling and statistical computing
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Ranking and selection procedures using standardized time series
WSC '85 Proceedings of the 17th conference on Winter simulation
Indifference-zone selection procedures for choosing the best airspace configuration
WSC '88 Proceedings of the 20th conference on Winter simulation
Selection procedures with standardized time series variance estimators
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
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This paper develops two extensions of the Gupta-Santner restricted subset selection procedure. The (exact) procedure RE screens a set of k normal populations with unknown and unequal variances using independent random sampling within each alternative population in order to select a final subset of at most m alternatives; in the least favorable configuration of population means, there is the minimal probability P* that the selected subset includes the population with the largest mean. The simulation-oriented (heuristic) procedure RS similarly screens a set of k covariance stationary normal processes with unknown and nonidentical covariance structures such that the (correlated) sampling within each alternative process is carried out independently. A rigorous development is given for procedure RE together with appropriate tables of constants required to apply the rule. The experimental performance of procedure RS is summarized for a wide variety of stationary autoregressive-moving average processes.