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Smooth Tests of Goodness of Fit

Smooth Tests of Goodness of Fit

          
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About the Book

In this fully revised and expanded edition of Smooth Tests of Goodness of Fit, the latest powerful techniques for assessing statistical and probabilistic models using this proven class of procedures are presented in a practical and easily accessible manner. Emphasis is placed on modern developments such as data-driven tests, diagnostic properties, and model selection techniques. Applicable to most statistical distributions, the methodology described in this book is optimal for deriving tests of fit for new distributions and complex probabilistic models, and is a standard against which new procedures should be compared.

New features of the second edition include:

  • Expansion of the methodology to cover virtually any statistical distribution, including exponential families
  • Discussion and application of data-driven smooth tests
  • Techniques for the selection of the best model for the data, with a guide to acceptable alternatives
  • Numerous new, revised, and expanded examples, generated using R code

Smooth Tests of Goodness of Fit is an invaluable resource for all methodological researchers as well as graduate students undertaking goodness-of-fit, statistical, and probabilistic model assessment courses. Practitioners wishing to make an informed choice of goodness-of-fit test will also find this book an indispensible guide.

Reviews of the first edition:

"This book gives a very readable account of the smooth tests of goodness of fit. The book can be read by scientists having only an introductory knowledge of statistics. It contains a fairly extensive list of references; research will find it helpful for the further development of smooth tests." --T.K. Chandra, Zentralblatt für Mathematik und ihre Grenzgebiete, Band 73, 1/92'

"An excellent job of showing how smooth tests (a class of goodness of fit tests) are generally and easily applicable in assessing the validity of models involving statistical distributions....Highly recommended for undergraduate and graduate libraries." --Choice

"The book can be read by scientists having only an introductory knowledge of statistics. It contains a fairly extensive list of references; researchers will find it helpful for the further development of smooth tests."--Mathematical Reviews

"Very rich in examples . . . Should find its way to the desks of many statisticians." --Technometrics


About the Author: John Rayner is a Professor of Statistics at theUniversity of Newcastle (Australia). He obtained his BA and MA from theUniversity of Sydney and PhD from theUniversity ofWollongong. He has held appointments at the Universities of New England, Otago, and Wollongong in addition to Newcastle. John is Associate Editor of the Journal of Applied Mathematics and Decision Sciences. He has over 100 publications in the fields of statistical model assessment and nonparametric statistics, has co-edited five books/proceedings/special journal issues and authored three research books.

Olivier Thas is an Associate Professor in biostatistics atGhentUniversity (Belgium). He joined the academic staff there in 1995 as a research assistant and was promoted to lecturer in 2001 after successful completion of his PhD in applied biological sciences. To-date Thas has ca. 50 publications in peer reviewed journals.

John Best is a Conjoint Academic at theUniversity ofNewcastle. After a number of years at the Commonwealth Bureau of Meterology (1967/70) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) (1970-2001) he accepted an honorary principal research fellowship at theUniversity ofWollongong (2001), where he also obtained his PhD in 1999. Best has over 100 scientific publications to his name.


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Product Details
  • ISBN-13: 9780470824443
  • Publisher: Wiley
  • Publisher Imprint: Wiley
  • Height: 220 mm
  • Returnable: Y
  • Spine Width: 23 mm
  • ISBN-10: 0470824441
  • Publisher Date: 29 Jan 2010
  • Binding: Online resource
  • Language: English
  • Series Title: Wiley Series in Probability and Statistics
  • Width: 154 mm


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