A Primer on Statistical Distributions 1st Edition by Narayanaswamy Balakrishnan (PDF)

Description

Designed as an introduction to statistical distribution theory. * Includes a first chapter on basic notations and definitions that are essential to working with distributions. * Remaining chapters are divided into three parts: Discrete Distributions, Continuous Distributions, and Multivariate Distributions. * Exercises are incorporated throughout the text in order to enhance understanding of materials just taught.

User’s Reviews

Editorial Reviews: Review …a very interesting and useful book…highly recommended… — Choice, Vol. 41, No. 5 January 2004″…a good reference for statisticians to have available on their bookshelf…it would not be difficult to build an interesting and relevant course around this book that would be highly beneficial to our students…” (Journal of the American Statistical Association, June 2004) “…a very interesting and useful book…highly recommended…” (Choice, Vol. 41, No. 5, January 2004) From the Inside Flap Introducing the perfect, all-in-one primer on statistical distributions Statistical distributions, along with their properties and interrelationships, are a central part of advanced statistics. However, most statistics textbooks only devote a few chapters to basic statistical distributions–such as binomial, Poisson, exponential, and normal–and stop short of covering other important distributions geared toward upper-level statistics courses.That’s where A Primer on Statistical Distributions makes its mark. Specifically tailored to the introductory course on statistical distributions, this unmatched resource takes a more balanced, all-inclusive approach than similar texts. In page after page, you’ll find a valuable review of often-overlooked distributions, including geometric, negative binomial, hypergeometric, Pareto, beta, gamma, chi-square, logistic, Cauchy, Laplace, extreme value, multinomial, Dirichlet, and multivariate normal.A Primer on Statistical Distributions begins with an informative first chapter on preliminary notations, definitions, and the concepts that are necessary to work effectively with distributions. The basic topics covered in this introductory chapter include distribution types, generating functions, shape characteristics, entropy, random vectors, conditional distributions, and regressions. Subsequent chapters are divided into three parts: discrete distributions, continuous distributions, and multivariate distributions. Each chapter includes many skill-building exercises that provide a helpful review of the material just discussed. And the book also contains an appendix with engaging biographical sketches of some of the leading minds behind the development of statistical distributions theory.A Primer on Statistical Distributions is not only ideal for students and professionals in statistics, it can also benefit individuals in applied areas such as psychology, geography, economics, and engineering, and even professionals in need of a logically organized, comprehensive reference to statistical distributions. It all adds up to a text that no one utilizing statistical distributions should be without. From the Back Cover Introducing the perfect, all-in-one primer on statistical distributions Statistical distributions, along with their properties and interrelationships, are a central part of advanced statistics. However, most statistics textbooks only devote a few chapters to basic statistical distributions–such as binomial, Poisson, exponential, and normal–and stop short of covering other important distributions geared toward upper-level statistics courses.That’s where A Primer on Statistical Distributions makes its mark. Specifically tailored to the introductory course on statistical distributions, this unmatched resource takes a more balanced, all-inclusive approach than similar texts. In page after page, you’ll find a valuable review of often-overlooked distributions, including geometric, negative binomial, hypergeometric, Pareto, beta, gamma, chi-square, logistic, Cauchy, Laplace, extreme value, multinomial, Dirichlet, and multivariate normal.A Primer on Statistical Distributions begins with an informative first chapter on preliminary notations, definitions, and the concepts that are necessary to work effectively with distributions. The basic topics covered in this introductory chapter include distribution types, generating functions, shape characteristics, entropy, random vectors, conditional distributions, and regressions. Subsequent chapters are divided into three parts: discrete distributions, continuous distributions, and multivariate distributions. Each chapter includes many skill-building exercises that provide a helpful review of the material just discussed. And the book also contains an appendix with engaging biographical sketches of some of the leading minds behind the development of statistical distributions theory.A Primer on Statistical Distributions is not only ideal for students and professionals in statistics, it can also benefit individuals in applied areas such as psychology, geography, economics, and engineering, and even professionals in need of a logically organized, comprehensive reference to statistical distributions. It all adds up to a text that no one utilizing statistical distributions should be without. About the Author N. BALAKRISHNAN, PhD, is Professor of Mathematics and Statistics at McMaster University in Hamilton, Ontario, Canada. V. B. NEVZOROV, PhD, DS, is Professor of Probability and Statistics at St. Petersburg State University in St. Petersburg, Russia. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐The book was as described and a timely delivery without damage.

⭐This book is a nearly exhaustive treatment of all the different probability distributions and state spaces and measurable subsets.The work covers discrete probability distributions and continuous probability distributions and includes explication of Bernoulli, Rademacher, binomial, and the degenerate distribution (my favourite! With random input into a deterministic function!). Others include hypergeometric distribution, Zipf distribution. And other power laws, Boltzmann, Gibbs, Maxwell-Boltzmann, Bose-Einstein, and Fermi-Dirac distributions (big in physics).The Poisson distribution is both not a fish and useless for prediction of fish stock scales, but can be useful for how high fish jump. Skellaman and Yule-Simon too (mini-Poisson).The logarithmic (series) distributions and the negative binomial distribution, get their own treatment, as does parabolic fractal distributions (Mandelbrot ahoy!).The zeta distribution is covered well and its application to physical sciences, mechanics, and construction and robustness of materials should attract new interest in today’s world of collapsed bridges.Special case distributions like Beta, truncated normal, Kent, Weibulll are treated okay, and standard textbook chi, noncentral chi, gama, Levy, Wald, Rice, Rayleigh, Pareto (important!) and Gumble are here too.Cauchy, Fisher-Tppett and joint distributions are also covered as well as some indications of copula, but naturally refer to other texts.

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