USING EXCURSIONS TO ANALYZE SIMULATION OUTPUT

2010 ◽  
Vol 24 (2) ◽  
pp. 201-218
Author(s):  
James M. Calvin
Keyword(s):  
Time Series ◽  
Steady State ◽  
Memory Requirement ◽  
Analysis Problem ◽  
Output Analysis ◽  
Simulation Output ◽  
Average Variance ◽  
Time Average ◽  
Steady State Simulation ◽  
Functional Central Limit

We consider the steady-state simulation output analysis problem for a process that satisfies a functional central limit theorem. We construct an estimator for the time-average variance constant that is based on excursions of a process above the minimum. The resulting estimator does not require a fixed run length, and the memory requirement can be dynamically bounded. Standardized time series methods based on excursions are also described.

SIMULATION OF PROCESSES WITH MULTIPLE REGENERATION SEQUENCES

2000 ◽  
Vol 14 (2) ◽  
pp. 179-201 ◽  
Author(s):  
James M. Calvin ◽  
Marvin K. Nakayama
Keyword(s):  
Performance Measures ◽  
Hitting Times ◽  
Efficiency Improvement ◽  
Additional Structure ◽  
Output Analysis ◽  
Average Variance ◽  
Time Average ◽  
Regenerative Simulation ◽  
Regeneration Times ◽  
Single Sequence

The classical regenerative method of simulation output analysis exploits the regenerative structure of a stochastic process to break up a path into independent and identically distributed cycles based on a single sequence of regeneration times. If a process is regenerative with respect to more than one sequence of regeneration times, the classical regenerative method does not exploit the additional structure, and the variance of the resulting estimator for certain performance measures (e.g., the time-average variance constant) can vary greatly, depending on the particular regeneration sequence chosen. In a previous article, we introduced an efficiency-improvement technique for regenerative simulation of processes having two sequences of regeneration times based on permuting regenerative cycles associated with the second sequence of regeneration points. In this article, we show how to exploit more than two regeneration sequences. In particular, for birth–death Markov chains, the regenerations associated with hitting times to each state can all be exploited. We present empirical results that show significant variance reductions in some cases, and the results seem to indicate that the permuted estimator for the time-average variance constant can have a variance that is independent of the primary regeneration sequence used to run the simulation.

A Sequential Stopping Rule for a Steady-State Simulation Based on Time-Series Forecasting

SIMULATION ◽  
2002 ◽  
Vol 78 (11) ◽  
pp. 643-654 ◽  
Author(s):  
Gerald T. Mackulak ◽  
Sungmin Park ◽  
John W. Fowler ◽  
Sonia E. Leach ◽  
J. Bert Keats
Keyword(s):  
Time Series ◽  
Steady State ◽  
Time Series Forecasting ◽  
Stopping Rule ◽  
Simulation Based ◽  
Steady State Simulation

Simulation Output Analysis Using Standardized Time Series

1990 ◽  
Vol 15 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Peter W. Glynn ◽  
Donald L. Iglehart
Keyword(s):  
Time Series ◽  
Output Analysis ◽  
Simulation Output
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