The focus of this book is on the birth and historical development of permutation statistical methods from the early 1920s to the near present. Beginning with the seminal contributions of R.A. Fisher, E.J.G. Pitman, and others in the 1920s and 1930s, permutation statistical methods were initially introduced to validate the assumptions of classical statistical methods.
Permutation methods have advantages over classical methods in that they are optimal for small data sets and non-random samples, are data-dependent, and are free of distributional assumptions. Permutation probability values may be exact, or estimated via moment- or resampling-approximation procedures. Because permutation methods are inherently computationally-intensive, the evolution of computers and computing technology that made modern permutation methods possible accompanies the historical narrative.
Permutation analogs of many well-known statistical tests are presented in a historical context, includingmultiple correlation and regression, analysis of variance, contingency table analysis, and measures of association and agreement. A non-mathematical approach makes the text accessible to readers of all levels.
表中的内容
Preface.- 1.Introduction.- 2.1920–1939.- 3.1940–1959.- 4.1960–1979.- 5.1980–2000.- 6.Beyond 2000.- Epilogue.- References.- Acronyms.- Name Index.- Subject Index.
关于作者
Kenneth J. Berry is Professor of Sociology at Colorado State University.
Janis E. Johnston is a Social Science Policy Analyst with the United States government in Washington, D.C.
Paul W. Mielke, Jr. is Professor Emeritus of Statistics at Colorado State University and Fellow of the American Statistical Association.
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