Kenneth J. Berry, Mielke, Jr., Paul W., Janis E Johnston

Permutation Statistical Methods

An Integrated Approach

• Broschiertes Buch

Produktbeschreibung

This research monograph provides a synthesis of a number of statistical tests and measures, which, at first consideration, appear disjoint and unrelated. Numerous comparisons of permutation and classical statistical methods are presented, and the two methods are compared via probability values and, where appropriate, measures of effect size.

Permutation statistical methods, compared to classical statistical methods, do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity of variance, and depend only on the data at hand. This text takes a unique approach to explaining statistics by integrating a large variety of statistical methods, and establishing the rigor of a topic that to many may seem to be a nascent field in statistics. This topic is new in that it took modern computing power to make permutation methods available to people working in the mainstream of research.
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Produktdetails

• Verlag: Springer / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-80419-4
• Softcover reprint of the original 1st ed. 2016
• Seitenzahl: 644
• Erscheinungstermin: 27. Mai 2018
• Englisch
• Abmessung: 235mm x 155mm x 35mm
• Gewicht: 961g
• ISBN-13: 9783319804194
• ISBN-10: 3319804197
• Artikelnr.: 53577020

Inhaltsangabe

Preface.- 1.Introduction.- 2.Completely Randomized Data.- 3.Randomized Designs: Interval Data.- 4.Regression Analysis of Interval Data.- 5.Randomized Designs: Ordinal Data, I.- 6.Randomized Designs: Ordinal Data, II.- 7.Randomized Designs: Nominal Data.- 8.Randomized Designs: Nominal Data.- 9.Randomized Block Designs: Interval Data.- 10.Randomized Block Designs: Ordinal Data.- 11.Randomized Block Designs: Nominal Data.- Epilogue.- References.- Author Index.- Subject Index.

Rezensionen

"This book summarizes the applications of the MRPP done by the authors to various statistical problems ... . This book may be useful for researchers who are interested in extending the MRPP to other types of data and statistical problems, for example, survival data with possible censoring of observations." (Dongsheng Tu, zbMATH 1358.62011, 2017)