Confidence Intervals Using the Regenerative Method for Simulation Output Analysis PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Confidence Intervals Using the Regenerative Method for Simulation Output Analysis PDF full book. Access full book title Confidence Intervals Using the Regenerative Method for Simulation Output Analysis by P. W. Glynn. Download full books in PDF and EPUB format.
Author: P. W. Glynn
Publisher:
ISBN:
Category :
Languages : en
Pages : 8
Get Book Here
Book Description
The regenerative method is a mathematically rigorous method for obtaining confidence intervals for steady state parameters. In this paper the qualitative structure of asymptotic confidence intervals is discussed in general. This structure is then specialized to confidence intervals for steady state parameters produced by the regenerative method. Keywords include: Approximation error; Regenerative simulation; Confidence intervals; Mean and variance of confidence interval width.
Author: P. W. Glynn
Publisher:
ISBN:
Category :
Languages : en
Pages : 8
Get Book Here
Book Description
The regenerative method is a mathematically rigorous method for obtaining confidence intervals for steady state parameters. In this paper the qualitative structure of asymptotic confidence intervals is discussed in general. This structure is then specialized to confidence intervals for steady state parameters produced by the regenerative method. Keywords include: Approximation error; Regenerative simulation; Confidence intervals; Mean and variance of confidence interval width.
Author: M. A. Crane
Publisher: Springer
ISBN:
Category : Computers
Languages : en
Pages : 128
Get Book Here
Book Description
The purpose of this report is to provide an introduction to the regenerative method for simulation analysis. The simulations are simulations of stochastic systems, i.e., systems with random elements. The regenerative approach leads to a statistical methodology for analyzing the output of those simulations which have the property of 'starting afresh probabilistically' from time to time. The class of such simulations is very large and very important, including simulations of a broad variety of queues and queueing networks, inventory systems, inspection, maintenance, and repair operations, and numerous other situations.
Author: Yung-Li Lily Jow
Publisher:
ISBN:
Category : Digital computer simulation
Languages : en
Pages : 222
Get Book Here
Book Description
As the use of computer simulation becomes more important in the study of complex phenomena, the need to develop theoretically sound and computationally efficient methods for simulation output analysis becomes more pressing. The autoregressive method proposed in this paper uses techniques developed for time series analysis to provide both point and interval estimates for parameters associated with the steady-state distribution. The major advantage of the autoregressive method is obvious. It serves as a black box; users provide the simulation output sequence, the black box will produce results automatically. Furthermore, it seems that the autoregressive method applies to a much broader class of stochastic processes than the regenerative method does. With the generalization to multidimensional processes, the method enables us to apply variance reduction techniques to get more accurate point estimates along with more precise interval estimates. The disadvantages of the autoregressive method are that the covariance matrix obtained by the autoregressive method is just an approximation for the covariance matrix present in the central limit theorem used to construct confidence intervals, and the assumptions put on the system are stricter than we would like.
Author: Peter W. Glynn
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
Get Book Here
Book Description
Coverage error asymptotics for confidence intervals arising in simulation are discussed. Asymptotic expansions, to order 0(n-1) (n is the sample size), are given for confidence intervals associated with sequences of independent and identically distributed random variables, as well as regenerative processes. Implications for simulation are emphasized. (Author).
Author: Michael F. Oltmanns
Publisher:
ISBN:
Category : Confidence intervals
Languages : en
Pages : 230
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category : Military research
Languages : en
Pages : 284
Get Book Here
Book Description
Author: Peter W. Glynn
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
Get Book Here
Book Description
Simulation is a commonly used method of analysis for studying complex stochastic systems. Often, the parameter of interest to the simulator can be estimated by more than one quantity. When more than one estimator exists, it is desirable to use the more stable estimate, namely the one with the lesser variance. In this paper, the authors consider a class of stochastic processes which enjoy cyclic regenerative structure - such systems often arise, for example, in analysis of queues. They study a family of estimators and determine precise conditions under which the estimators are asymptotically valid. They also obtain a closed-form solution for the minimum variance estimate in the family, and prove that this estimator will often be superior to the standard regenerative estimator for the simulation.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 151
Get Book Here
Book Description
Discrete-event simulation is computer modeling of stochastic, dynamic systems. The Kalman filter is a Bayesian stochastic estimation algorithm. Because of the correlated nature of simulation output, it is difficult to apply the methods of classical statistics directly when constructing confidence intervals of discrete-event simulation parameters. Through the determination of a dynamics equation and application of the Kalman filter to simulation output data, three new confidence interval construction techniques have been developed. One technique obtains an estimate of the mean value and its associated variance from an estimated Kalman filter. The second technique utilizes Multiple Model Adaptive Estimation (MMAE) techniques to obtain an estimate of the simulation output's mean value and its associated variance. The third technique also uses MMAE, but constructs a nonsymmetric confidence interval using the final MMAE filter probabilities. The purpose of this research was twofold. The first objective was to explore these new confidence interval construction techniques based on the information provided by Kalman filters. The second objective was to contrast these Kalman filter approaches to several accepted approaches. Both of these objectives were achieved and excellent results were obtained. In particular, a Monte Carlo analysis demonstrated that the third technique produced intervals that achieved nominal coverage rates with, when compared to currently accepted techniques, smaller average half widths and lower variability. Confidence-Intervals, Kalman Filters, Discrete-Event Simulation, Statistical Analysis, Computer Simulation.
Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 1572
Get Book Here
Book Description
Author: Gerald S. Shedler
Publisher: Elsevier
ISBN: 0080925723
Category : Mathematics
Languages : en
Pages : 412
Get Book Here
Book Description
Simulation is a controlled statistical sampling technique that can be used to study complex stochastic systems when analytic and/or numerical techniques do not suffice. The focus of this book is on simulations of discrete-event stochastic systems; namely, simulations in which stochastic state transitions occur only at an increasing sequence of random times. The discussion emphasizes simulations on a finite or countably infinite state space.* Develops probabilistic methods for simulation of discrete-event stochastic systems* Emphasizes stochastic modeling and estimation procedures based on limit theorems for regenerative stochastic processes* Includes engineering applications of discrete-even simulation to computer, communication, manufacturing, and transportation systems* Focuses on simulations with an underlying stochastic process that can specified as a generalized semi-Markov process* Unique approach to simulation, with heavy emphasis on stochastic modeling* Includes engineering applications for computer, communication, manufacturing, and transportation systems
