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Permutation statistical methods = an...

Berry, Kenneth J.

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Permutation statistical methods = an integrated approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Permutation statistical methods/ by Kenneth J. Berry, Paul W. Mielke, Jr., Janis E. Johnston.
其他題名:	an integrated approach /
作者:	Berry, Kenneth J.
其他作者:	Mielke, Jr., Paul W.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xx, 622 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Mathematical statistics. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-28770-6
ISBN:	9783319287706

Permutation statistical methods = an integrated approach /
Berry, Kenneth J.

Permutation statistical methodsan integrated approach /[electronic resource] :by Kenneth J. Berry, Paul W. Mielke, Jr., Janis E. Johnston. - Cham :Springer International Publishing :2016. - xx, 622 p. :ill., digital ;24 cm.

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.

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

ISBN: 9783319287706

Standard No.: 10.1007/978-3-319-28770-6doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

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505 0 $a 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.
520 $a 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. This research monograph addresses a statistically-informed audience, and can also easily serve as a textbook in a graduate course in departments such as statistics, psychology, or biology. In particular, the audience for the book is teachers of statistics, practicing statisticians, applied statisticians, and quantitative graduate students in fields such as psychology, medical research, epidemiology, public health, and biology.
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