Description
Book SynopsisAlthough power method polynomials based on the standard normal distributions have been used in many different contexts for the past 30 years, it was not until recently that the probability density function (pdf) and cumulative distribution function (cdf) were derived and made available. Focusing on both univariate and multivariate nonnormal data generation, Statistical Simulation: Power Method Polynomials and Other Transformations presents techniques for conducting a Monte Carlo simulation study. It shows how to use power method polynomials for simulating univariate and multivariate nonnormal distributions with specified cumulants and correlation matrices.
The book first explores the methodology underlying the power method, before demonstrating this method through examples of standard normal, logistic, and uniform power method pdfs. It also discusses methods for improving the performance of a simulation based on power method polynomials. The book then d
Trade Review
Headrick’s book is a valuable addition to the simulation world in the sense that it is the first systematic book on power method polynomials … this book has potential to be one of the key sources for researchers who are involved in random number generation and Monte Carlo studies. … it can serve and seems to be designed as a supplemental text in graduate level simulation- and computation-oriented courses. … Mathematica functions are provided throughout the book, which is a nice feature … Headrick’s book is a fairly major contribution to the literature … . I would highly recommend this book to anyone who deals with random number generation and more generally with Monte Carlo simulation and statistical computing.
—Journal of Statistical Software, Vol. 43, September 2011
This interesting monograph concerns the use of power method polynomials in the context of simulating univariate and multivariate nonnormal distributions with given cumulant and correlation structure. Its strength is in the fact that the power method, g-and-h transformations and GLD transformations are capable of simulation multivariate nonnormal continuous distributions specified by its cumulants and correlation matrices. … —Zentralblatt MATH 1187
Table of ContentsIntroduction. The Power Method Transformation. Using the Power Method Transformation. Simulating More Elaborate Correlation Structures. Other Transformations: The g-and-h and GLD Families of Distributions. References. Index.
