In this article, we present a general framework to construct forecasts using simulation. This framework allows us to incorporate available data into a forecasting model in order to assess parameter uncertainty through a posterior distribution, which is then used to estimate a point forecast in...

We propose and analyze a new class of estimators for the variance parameter of a steady-state simulation output process. The new estimators are computed by averaging individual estimators from “folded” standardized time series based on overlapping batches composed of consecutive...

A simulation study consists of several steps such as data collection, codingand model verification, model validation, experimental design, output data analysis,and implementation. Our research concentrates on output data analysis. In this field,many researchers have studied how to construct...

We study the estimation of steady-state performance measures from an \frak{R}^d-valued stochastic process Y = {Y(t): t \ge 0} representing the output of a simulation. In many applications, we may be interested in the estimation of a steady-state performance measure that cannot be expressed as a...

When an estimator of the variance of the sample mean is parameterized by batch size, one approach for selecting batch size is to pursue the minimal mean squared error (mse). We show that the convergence rate of the variance of the sample mean, and the bias of estimators of the variance of the...

We develop new asymptotically valid confidence interval estimators (CIE's) for the underlying mean of a stationary simulation process. The new estimators are weighted generalizations of Schruben's standardized time series area CIE. We show that the weighted CIE's have the same asymptotic...