Nilay Tanik Argon

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For a steady-state simulation output process, we formulate efficient algorithms to compute certain estimators of the process variance parameter (i.e., the sum of covariances at all lags), where the estimators are derived in principle from overlapping batches separately and then averaged over all such batches. The algorithms require order-of-sample-size work(More)
The most widely used standard for mass-casualty triage, START, relies on a fixed-priority ordering among different classes of patients, and does not explicitly consider resource limitations or the changes in survival probabilities with respect to time. We construct a fluid model of patient triage in a mass-casualty incident that incorporates these factors(More)
In the aftermath of mass-casualty events, key resources (such as ambulances and operating rooms) can be overwhelmed by the sudden jump in patient demand. To ration these resources, patients are assigned different priority levels, which is called triage. According to triage protocols in place, each patient's priority level is determined based on that(More)
Independent replications (IR) and batch means (BM) are two of the most widely used variance-estimation methods for simulation output analysis. Alexopoulos and Goldsman conducted a thorough examination of IR and BM; and Andradóttir and Argon proposed the method of replicated batch means (RBM), which combines good characteristics of IR and BM. This(More)
We provide asymptotic expressions for the expected value and variance of the replicated batch means variance estimator when the stochastic process being simulated has an additive initial transient. These expressions explicitly show how the initial transient and the autocorrelation in the data affect the performance of the estimator. We apply our results to(More)
We examine properties of overlapped versions of the standardized time series area and Cramér-von Mises estimators for the variance parameter of a stationary stochastic process, e.g., a steady-state simulation output process. We find that the overlapping estimators have the same bias properties as, but lower variance than, their nonoverlapping(More)