PDF Ebook Testing For Normality (Statistics: a Series of Textbooks and Monogrphs), by Henry C. Thode
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Testing For Normality (Statistics: a Series of Textbooks and Monogrphs), by Henry C. Thode
PDF Ebook Testing For Normality (Statistics: a Series of Textbooks and Monogrphs), by Henry C. Thode
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Describes the selection, design, theory, and application of tests for normality. Covers robust estimation, test power, and univariate and multivariate normality. Contains tests ofr multivariate normality and coordinate-dependent and invariant approaches.
- Sales Rank: #4421507 in Books
- Published on: 2002-01-25
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 6.25" w x 1.00" l, 1.73 pounds
- Binding: Hardcover
- 368 pages
Review
…this clearly written comprehensive and thoroughly researched work provides much useful information and should probably be on the shelves of every research-oriented department's library.
- Mathematical Reviews, Issue 2004d
Most helpful customer reviews
26 of 27 people found the following review helpful.
extensive coverage of goodness of fit for the normal family
By Michael R. Chernick
Thode does a masterful job of organizing the vast literature on testing normality. He has extensive bibliographies at the end of each chapter. He covers both univariate and multivariate tests.
It is designed for practitioners with most of the theory avoided but all the references necessary to learn the theory are given. The title does not do the book justice. He includes outlier detection (univariate and multivariate) and testing for mixtures of normals. Even influence function measures that do not necessarily relate to the Gaussian model are discussed. To my pleasant surprise some of my work on the use of influence functions to detect multivariate outliers is mentioned briefly.
A nice feature of the book is the thoughtfully chosen quotes from statisticians at the beginning of each chapter. The first one provides the theme for the book. It is a quote from Egon Pearson in 1935 where Pearson (who with Neyman worked on optimal tests) says that it is permissible for statisticians to use less than efficient tests when simplicity or ease of computation are a consideration.
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