Searching for important factors: sequential bifurcation under uncertainty
Proceedings of the 29th conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Proceedings of the 33nd conference on Winter simulation
Proceedings of the 35th conference on Winter simulation: driving innovation
Military applications of agent-based simulations
WSC '04 Proceedings of the 36th conference on Winter simulation
Design and Analysis of Experiments
Design and Analysis of Experiments
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
A hybrid method for simulation factor screening
Proceedings of the 38th conference on Winter simulation
Defense and homeland security applications of multi-agent simulations
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Controlled sequential bifurcation for software reliability study
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Important factors in screening for colorectal cancer
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Two-phase screening procedure for simulation experiments
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Efficient experimental design tools for exploring large simulation models
Computational & Mathematical Organization Theory
| Hi-index | 0.00 |
Analysts examining complex simulation models often conduct screening experiments to identify the most important factors. Controlled sequential bifurcation (CSB) is a screening procedure, developed specifically for simulation experiments, that uses a sequence of hypothesis tests to classify the factors as either important or unimportant. CSB controls the probability of Type I error for each factor, and the power at each bifurcation step, under heterogeneous variance conditions. CSB does, however, require the user to correctly state the directions of the effects prior to running the experiments. Experience indicates that this can be problematic with complex simulations.We propose a hybrid two-phase approach, FF-CSB, to relax this requirement. Phase 1 uses an efficient fractional factorial experiment to estimate the signs and magnitudes of the effects. Phase 2 uses these results in controlled sequential bifurcation. We describe this procedure and provide an empirical evaluation of its performance.