Tools for simulation output analysis

This technical report presents a description of the output data files and the tools used to validate and to extract information from the output data files generated by the Repeater-Based Hybrid Wired/Wireless Network Simulator and the Bridge-Based Hybrid Wired/Wireless Network Simulator

Estimating population means in covariance stationary process

In simple random sampling, the basic assumption at the stage of estimating the standard error of the sample mean and constructing the corresponding confidence interval for the population mean is that the observations in the sample must be independent. In a number of cases, however, the validity of this assumption is under question, and as examples we mention the cases of generating dependent quantities in Jackknife estimation, or the evolution through time of a social quantitative indicator in longitudinal studies. For the case of covariance stationary processes, in this paper we explore the consequences of estimating the standard error of the sample mean using however the classical way based on the independence assumption. As criteria we use the degree of bias in estimating the standard error, and the actual confidence level attained by the confidence interval, that is, the actual probability the interval to contain the true mean. These two criteria are computed analytically under different sample sizes in the stationary ARMA(1,1) process, which can generate different forms of autocorrelation structure between observations at different lags.Jackknife estimation; ARMA; Longitudinal data; Actual confidence level

Application of Object-oriented Programming in Simulation: A Simulation of Case Study Using Microsoft Visual C++

In this thesis, a prototype simulation environment is introduced. Simulation has always been important for systems analysis. The original idea of this thesis stems from the fact that more flexible simulation programming tools are required by the modern analysis. Six simulation classes are implemented in the thesis to support simple simulation cases and Microsoft Visual C++ window classes are used to build a user friendly interface. A simulation output class is also implemented to conduct simple simulation output analysis. Based on this work, more classes and features can be added to make the simulation environment more powerful, so that the simulation environment can support different simulation situations

Confidence intervals in stationary autocorrelated time series

In this study we examine in covariance stationary time series the consequences of constructing confidence intervals for the population mean using the classical methodology based on the hypothesis of independence. As criteria we use the actual probability the confidence interval of the classical methodology to include the population mean (actual confidence level), and the ratio of the sampling error of the classical methodology over the corresponding actual one leading to equality between actual and nominal confidence levels. These criteria are computed analytically under different sample sizes, and for different autocorrelation structures. For the AR(1) case, we find significant differentiation in the values taken by the two criteria depending upon the structure and the degree of autocorrelation. In the case of MA(1), and especially for positive autocorrelation, we always find actual confidence levels lower than the corresponding nominal ones, while this differentiation between these two levels is much lower compared to the case of AR(1).Covariance stationary time series; Variance of the sample mean; Actual confidence level

Theoretical and Empirical Investigation of Fourier Trajectory Analysis for System Discrimination

With few exceptions, simulation output analysis has focused on static characterizations, to determine a property of the steady-state distribution of a performance metric such as a mean, a quantile, or the distribution itself. Analyses often seek to overcome difficulties induced by autocorrelation of the output stream. But sample paths generated by stochastic simulation exhibit dynamic behavior that is characteristic of system structure and associated distributions. In this technical report, we investigate these dynamic characteristics, as captured by the Fourier transform of a dynamic simulation trajectory. We find that Fourier coefficient magnitudes can have greater discriminatory power than the usual test statistics, and with simpler analysis resulting from the statistical independence of coefficient estimates at different frequencies. Theoretical and Empirical results are provided

A General Simulation Framework for Supply Chain Modeling: State of the Art and Case Study

Nowadays there is a large availability of discrete event simulation software that can be easily used in different domains: from industry to supply chain, from healthcare to business management, from training to complex systems design. Simulation engines of commercial discrete event simulation software use specific rules and logics for simulation time and events management. Difficulties and limitations come up when commercial discrete event simulation software are used for modeling complex real world-systems (i.e. supply chains, industrial plants). The objective of this paper is twofold: first a state of the art on commercial discrete event simulation software and an overview on discrete event simulation models development by using general purpose programming languages are presented; then a Supply Chain Order Performance Simulator (SCOPS, developed in C++) for investigating the inventory management problem along the supply chain under different supply chain scenarios is proposed to readers.Comment: International Journal of Computer Science Issues online at http://ijcsi.org/articles/A-General-Simulation-Framework-for-Supply-Chain-Modeling-State-of-the-Art-and-Case-Study.ph

Batch means and spectral variance estimators in Markov chain Monte Carlo

Calculating a Monte Carlo standard error (MCSE) is an important step in the statistical analysis of the simulation output obtained from a Markov chain Monte Carlo experiment. An MCSE is usually based on an estimate of the variance of the asymptotic normal distribution. We consider spectral and batch means methods for estimating this variance. In particular, we establish conditions which guarantee that these estimators are strongly consistent as the simulation effort increases. In addition, for the batch means and overlapping batch means methods we establish conditions ensuring consistency in the mean-square sense which in turn allows us to calculate the optimal batch size up to a constant of proportionality. Finally, we examine the empirical finite-sample properties of spectral variance and batch means estimators and provide recommendations for practitioners.Comment: Published in at http://dx.doi.org/10.1214/09-AOS735 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

BAYESIAN FORECASTING USING STOCHASTIC SIMULATION

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 the form of a conditional (given the data) expectation. The uncertainty on the point forecast is assessed through the estimation of a conditional variance and a prediction interval. We discuss how to construct asymptotic confidence intervals to assess the estimation error for the estimators obtained using simulation. We illustrate how this approach is consistent with Bayesian forecasting by presenting two examples, and experimental results that confirm our analytical results are discussed.Forecasting; simulation output analysis; Bayesian estimation; quantile estimation.