Goodness of fit describes the validity of models involving statistical distribution of data, and smooth tests are a subset of these tests which are easy to apply and can be used in any situation in which there are relatively large sample sizes. Both concepts have become increasingly important with the advent of high-speed computers and the implementation of more complex models in the areas of probability and statistics. Written to be accessible to undergraduates with a working knowledge of statistics and calculus, this is an introductory reference work that should appeal to all professionals involved in statistical modelling.
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'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
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:
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.
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