The preparation of several short courses on distribution-free statisti cal methods for students at third and fourth year level in Australian universities led to the writing ofthis book. My criteria for the courses were, firstly, that the subject should have a clearly recognizable underlying common thread rather than appear to be a collection of isolated techniques. Secondly, some discussion of efficiency seemed essential, at a level where the students could appreciate the reasons for the types of calculations that are performed, and be able actually to do some of them. Thirdly, it seemed desirable to emphasize point and interval estimation rather more strongly than is the case in many of the fairly elementary books in this field. Randomization, or permutation, is the fundamental idea that connects almost all of the methods discussed in this book. Application of randomization techniques to original observations, or simple transformations of the observations, leads generally to conditionally distribution-free inference. Certain transformations, notably 'sign' and 'rank' transformations may lead to unconditionally distribution-free inference. An attendant advantage is that useful tabulations of null distributions of test statistics can be produced. In my experience students find the notion of asymptotic relative efficiency of testing difficult. Therefore it seemed worthwhile to give a rather informal introduction to the relevant ideas and to concentrate on the Pitman 'efficacy' as a measure of efficiency.
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"In summary, the book is both readable and informative. It probably covers more material than would normally be included in a standard course on distribution-free methods, but it would be a useful reference for any student following such a course." -The Statistician
Distribution-free statistical methods enable users to make statistical inferences with minimum assumptions about the population in question. They are widely used especially in the areas of medical and psychological research. This new edition is aimed at senior undergraduate and graduate level. It also includes a discussion of new techniques that have arisen as a result of improvements in statistical computing. Interest in estimation techniques has particularly grown and this section of the book has been expanded accordingly. Finally, Distribution-free Statistical Methods will induce more examples with actual data sets appearing in the text.
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