Article ID: | iaor19971670 |
Country: | United States |
Volume: | 42 |
Issue: | 4 |
Start Page Number: | 559 |
End Page Number: | 575 |
Publication Date: | Apr 1996 |
Journal: | Management Science |
Authors: | Bauer Kenneth W., Gallagher Mark A., Maybeck Peter S. |
Keywords: | statistics: inference, time series & forecasting methods |
Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for univariate output is proposed. This technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output’s steady-state mean. This MMAE implementation features the use, in parallel, of a bank of three Kalman filters. Each filter is constructed under a different assumption about the output’s steady-state mean. One of the filters assumes that the steady-state mean is accurately reflected by an estimate, called the ‘assumed steady-state mean’, taken from the last half of the simulation data. This filter is called the