Proceedings of the 32nd conference on Winter simulation
Input modeling: input modeling techniques for discrete-event simulations
Proceedings of the 33nd conference on Winter simulation
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Input modeling: input modeling
Proceedings of the 35th conference on Winter simulation: driving innovation
Building credible input models
WSC '04 Proceedings of the 36th conference on Winter simulation
Introduction to modeling and generating probabilistic input processes for simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
A simulation model of a helicopter ambulance service
WSC '05 Proceedings of the 37th conference on Winter simulation
Introduction to modeling and generating probabilistic input processes for simulation
Proceedings of the 38th conference on Winter simulation
An Automated Multiresolution Procedure for Modeling Complex Arrival Processes
INFORMS Journal on Computing
Introduction to modeling and generating probabilistic input processes for simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Introduction to modeling and generating probabilistic input processes for simulation
Proceedings of the 40th Conference on Winter Simulation
Real-time dynamic pricing for multiproduct models with time-dependent customer arrival rates
ACC'09 Proceedings of the 2009 conference on American Control Conference
Introduction to modeling and generating probabilistic input processes for simulation
Winter Simulation Conference
Estimation for nonhomogeneous Poisson processes from aggregated data
Operations Research Letters
Modeling clustered non-stationary Poisson processes for stochastic simulation inputs
Computers and Industrial Engineering
Data-driven simulation of complex multidimensional time series
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on simulation in complex service systems
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A nonparametric technique for estimating the cumulative intensity function of a nonhomogeneous Poisson process from one or more realizations on an interval is extended here to include realizations that overlap. This technique does not require any arbitrary parameters from the modeler, and the estimated cumulative intensity function can be used to generate a point process for simulation by inversion.