1st Edition

Revival: Simulation Methodology for Statisticians, Operations Analysts, and Engineers (1988)

By P. W. A. Lewis, Ed McKenzie Copyright 1989
    434 Pages
    by Chapman & Hall

    434 Pages
    by Chapman & Hall

    Students of statistics, operations research, and engineering will be informed of simulation methodology for problems in both mathematical statistics and systems simulation. This discussion presents many of the necessary statistical and graphical techniques.

    A discussion of statistical methods based on graphical techniques and exploratory data is among the highlights of Simulation Methodology for Statisticians, Operations Analysts, and Engineers.

    For students who only have a minimal background in statistics and probability theory, the first five chapters provide an introduction to simulation.

    Definition of Simulation
    Golden Rules and Principles of Simulation
    Modeling: Illustrative Examples and Problems
    The Modeling Aspect of Simulation
    Single-Server, Single-Input, First-In/First-Out (FIFO) Queue
    Multiple-Server, Single-Input Queue
    An Example from Statistics: The Trimmed t Statistic
    An Example from Engineering: Reliability of Series Systems
    A Military Problem: Proportional Navigation
    Comments on the Examples
    Crude (or Straightforward) Simulation and Monte Carlo
    Introduction: Pseudo-Random Numbers
    Crude Simulation
    Details of Crude Simulation
    A Worked Example: Passage of Ships Through a Mined Channel
    Generation of Random Permutations
    Uniform Pseudo-Random Variable Generation
    Introduction: Properties of Pseudo-Random Variables
    Historical Perspectives
    Current Algorithms
    Recommendations for Generators
    Computational Considerations
    The Testing of Pseudo-Random Number Generators
    Conclusions on Generating and Testing Pseudo-Random Number Generators
    Descriptions and Quantifications of Univariate Samples: Numerical Summaries
    Sample Moments
    Percentiles, the Empirical Cumulative Distribution Function, and Goodness-of-Fit Tests
    Descriptions and Quantifications of Univariate Samples: Graphical Summaries
    Numerical and Graphical Representations of the Probability Density Function
    Alternative Graphical Methods for Exploring Distributions
    Comparisons in Multifactor Simulations: Graphical and Formal Methods
    Graphical and Numerical Representation of Multifactor Simulation Experiments
    Specific Considerations for Statistical Simulation
    Summary and Computing Resources
    Assessing Variability in Univariate Samples: Sectioning, Jackknifing, and Bootstrapping
    Assessing Variability of Sample Means and Percentiles
    Sectioning to Assess Variability: Arbitrary Estimates from Non-Normal Samples
    Bias Elimination
    Variance Assessment with the Complete Jackknife
    Variance Assessment with the Bootstrap
    Simulation Studies of Confidence Interval Estimation Schemes
    Bivariate Random Variables: Definitions, Generation, and Graphical Analysis
    Specification and Properties of Bivariate Random Variables
    Numerical and Graphical Analyses for Bivariate Data
    The Bivariate Inverse Probability Integral Transform
    Ad Hoc and Model-Based Methods for Bivariate Random Variable Generation
    Variance Reduction
    Antithetic Variates: Induced Negative Correlation
    Control Variables
    Conditional Sampling
    Importance Sampling
    Stratified Sampling


    P. W. A. Lewis, Ed McKenzie