Overview

Throughout the physical and social sciences, researchers face the challenge of fitting statistical distributions to their data. Although the study of statistical modelling has made great strides in recent years, the number and variety of distributions to choose from-all with their own formulas, tables, diagrams, and general properties-continue to create problems. For a specific application, which of the dozens of distributions should one use? What if none of them fit well?Fitting Statistical Distributions helps answer those questions. Focusing on techniques used successfully across many fields, the authors present all of the relevant results related to the Generalized Lambda Distribution (GLD), the Generalized Bootstrap (GB), and Monte Carlo simulation (MC). They provide the tables, algorithms, and computer programs needed for fitting continuous probability distributions to data in a wide variety of circumstances-covering bivariate as well as univariate distributions, and including situations where moments do not exist. Regardless of your specific field-physical science, social science, or statistics, practitioner or theorist-Fitting Statistical Distributions is required reading. It includes wide-ranging applications illustrating the methods in practice and offers proofs of key results for those involved in theoretical development. Without it, you may be using obsolete methods, wasting time, and risking incorrect results.

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9781584880691

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Reviews

The generalized lambda family of distributions is a very broad family of continuous univariate probability distributions. The authors have been at the forefront in investigating this distributionthey thoroughly explore the relationship of the generalized lambda family of distributions to many commonly used families of distributionsprovide a thorough exploration of the generalized lambda family of distributions and its use in the fitting of data. Practitioners who wish to fit data with a generalized lambda distribution will find this book useful. Numerous examples with actual datasets illustrate the utility of the techniquesIn summary, the authors have presented a complete exploration of the use of a particular family of distributions in fitting data. - Thomas E. Wehrly, Texas A & M University, Technometrics, May 2002 In this outstanding treatise the GLD is explored in depth. The writing is clear and the mathematical analyses are easy to follow. -Telegraphic Reviews This book is clearly written, and provides an excellent summary of what is currently known about the GLD, and indeed the authors have made major contributions to this body of knowledge in the last few years --M. S. Ridout, Biometrics, June 2001

"""The generalized lambda family of distributions is a very broad family of continuous univariate probability distributions. The authors have been at the forefront in investigating this distributionthey thoroughly explore the relationship of the generalized lambda family of distributions to many commonly used families of distributionsprovide a thorough exploration of the generalized lambda family of distributions and its use in the fitting of data. Practitioners who wish to fit data with a generalized lambda distribution will find this book useful. Numerous examples with actual datasets illustrate the utility of the techniquesIn summary, the authors have presented a complete exploration of the use of a particular family of distributions in fitting data."" - Thomas E. Wehrly, Texas A & M University, Technometrics, May 2002 ""In this outstanding treatise the GLD is explored in depth. The writing is clear and the mathematical analyses are easy to follow."" -Telegraphic Reviews ""This book is clearly written, and provides an excellent summary of what is currently known about the GLD, and indeed the authors have made major contributions to this body of knowledge in the last few years"" --M. S. Ridout, Biometrics, June 2001"

Author Information

Karian, Zaven A.; Dudewicz, Edward J.

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