A Contingency Table Approach to Nonparametric Testing

Rayner, J.C.W.

ISBN 10: 1584881615 / ISBN 13: 9781584881612

Published by Chapman and Hall/CRC, 2000

About this Item

Nonparametric texts typically concentrate on location and linear-linear (correlation) tests, with a lesser focus on dispersion effects and linear-quadratic tests. Using a fresh approach, this book intends to unify and extend the popular, standard tests by linking them to tests based on models for data that can be presented in contingency tables. Num Pages: 264 pages, 20 black & white illustrations, 68 black & white tables. BIC Classification: PBT. Category: (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 235 x 156 x 20. Weight in Grams: 526. . 2000. 1st Edition. hardcover. . . . .

Seller Inventory # V9781584881612

Bibliographic Details

• Title: A Contingency Table Approach to Nonparametric Testing
• Publisher: Chapman and Hall/CRC
• Publication Date: 2000
• Language: English
• ISBN 10: 1584881615
• ISBN 13: 9781584881612
• Binding: Hardcover
• Edition: 1st Edition
• Condition: New

About this title

Synopsis

Most texts on nonparametric techniques concentrate on location and linear-linear (correlation) tests, with less emphasis on dispersion effects and linear-quadratic tests. Tests for higher moment effects are virtually ignored. Using a fresh approach, A Contingency Table Approach to Nonparametric Testing unifies and extends the popular, standard tests by linking them to tests based on models for data that can be presented in contingency tables. This approach unifies popular nonparametric statistical inference and makes the traditional, most commonly performed nonparametric analyses much more complete and informative. It also makes tied data easily handled, and almost exact Monte Carlo p-values can be obtained. With data in contingency tables, one can then calculate a Pearson-type, chi-squared statistic and its components. For univariate data, the initial tests based on these components detect mean differences between treatments. For bivariate data, they detect correlations. This approach leads to tests that detect variance, skewness, and higher moment differences between treatments with univariate data, and higher bivariate moment differences with bivariate data. Although the methods advanced in this book have their genesis in traditional nonparametrics, incorporating the power of modern computers makes the approach more complete and more valid than previously possible. The authors' unified treatment and readable style make the subject easy to follow and the techniques easily implemented, whether you are a fledgling or a seasoned researcher.

About the Author

J.C.W. Rayner, D.J. Best