Very practical, with numerous examples, this fully revised and updated new edition:
- Outlines new research, introduce new methodology and expand the contents to cover virtually any statistical distribution.
- Provides powerful techniques for assessment of statistical and probabilistic models, with a guide to acceptable alternatives
- Focuses on a class of procedures demonstrated to work well, presented in a manner that allows the development of assessment tools for new distributions and complex probabilistic models
- Includes online examples in R
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
Essential reading for Researchers and postgraduates carrying out research on goodness-of-fit, statistical and probabilistic model assessment and hypothesis testing.