Permutation Statistical Methods

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. lly-informed="" audience,="" and="" can="" also="" easily="" serve="" as="" textbook="" in="" graduate="" course="" departments="" such="" statistics,="" psychology,="" or="" biology.="" particular,="" the="" audience="" for="" book="" is="" teachers="" of="" practicing="" statisticians,="" applied="" quantitative="" students="" fields="" medical="" research,="" epidemiology,="" public="" health,="" biology.

Presents a methodological umbrella under which (1) disparate statistical methods are synthesized and integrated and (2) a number of new permutation statistical methods are developed Synthesizes and integrates a large number of existing classical statistics under a common mathematical function Provides computing algorithms for calculating permutation tests, and details the history of permutation statistical methods

Autorentext
Kenneth J. Berry earned his Ph.D. in Sociology at the University of Oregon and is Professor in the Department of Sociology at Colorado State University, Fort Collins, Colorado, USA. His research interests are in non-parametric and distribution-free statistical methods. Kenneth L. Kvamme earned his Ph.D. in Anthropology at the University of California, Santa Barbara and is Professor in the Department of Anthropology and Director of the Archeo-Imagining Laboratory at the University of Arkansas, Fayetteville, Arkansas, USA. His research interests include archeological computer applications, GIS, lithic technology, remote sensing, geophysical prospecting, and spatial analysis methods. Janis E. Johnston earned her Ph.D. in Sociology at Colorado State University and is a Senior Technical Advisor for the U.S. Government in Alexandria, Virginia, USA and a Faculty Affiliate in the Department of Sociology at Colorado State University, Fort Collins, Colorado,USA. Paul W. Mielke, Jr. earned his Ph.D. in Biostatistics at the University of Minnesota and was Emeritus Professor of Statistics at Colorado State University, Fort Collins, Colorado, USA, and Fellow of the American Statistical Association. His research interests were in multivariate statistics and permutation statistical methods. Paul Mielke passed away on 20 April 2019.

Inhalt
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.