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.
"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."
"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."
- Pearson's X test
- Asymptotically optimal tests
- Neyman smooth tests for simple null hypotheses
- Neyman smooth tests for categorized simple null hypotheses
- Neyman smooth tests for uncategorized composite null hypotheses
- Neyman smooth tests for categorized composite null hypotheses
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