Simulation-based estimation of proportions
Management Science
Simulation-based estimation of quantiles
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Experimental performance evaluation of histogram approximation for simulation output analysis
WSC '04 Proceedings of the 36th conference on Winter simulation
Determination of the "best" system that meets a limit standard
WSC '05 Proceedings of the 37th conference on Winter simulation
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
A Bayesian approach to analysis of limit standards
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
| Hi-index | 0.00 |
Limit standards are probability interval requirements for proportions. Simulation literature has focused on finding the confidence interval of the population proportion, which is inappropriate for limit standards. Further, some Frequentist approaches cannot be utilized for highly reliable models, or models which produce no or few non-conforming trials. Bayesian methods provide approaches that can be utilized for all limit standard models. We consider a methodology developed for Bayesian reliability analysis, where historical data is used to define the a priori distribution of proportions p, and the customer desired a posteriori maximum probability is utilized to determine sample size for a replication.