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Introduction to Robust Estimation and Hypothesis Testing (Statistical Modeling and Decision Science) - Hardcover
Synopsis
Introduction to Robust Estimating and Hypothesis Testing, 4th Editon, is a ‘how-to’ on the application of robust methods using available software. Modern robust methods provide improved techniques for dealing with outliers, skewed distribution curvature and heteroscedasticity that can provide substantial gains in power as well as a deeper, more accurate and more nuanced understanding of data. Since the last edition, there have been numerous advances and improvements. They include new techniques for comparing groups and measuring effect size as well as new methods for comparing quantiles. Many new regression methods have been added that include both parametric and nonparametric techniques. The methods related to ANCOVA have been expanded considerably. New perspectives related to discrete distributions with a relatively small sample space are described as well as new results relevant to the shift function. The practical importance of these methods is illustrated using data from real world studies. The R package written for this book now contains over 1200 functions.
New to this edition
- 35% revised content
- Covers many new and improved R functions
- New techniques that deal with a wide range of situations
- Extensive revisions to cover the latest developments in robust regression
- Covers latest improvements in ANOVA
- Includes newest rank-based methods
- Describes and illustrated easy to use software
"synopsis" may belong to another edition of this title.
Review
This work describes and illustrates modern robust methods for dealing with outliers, skewed distributions, heteroscedasticity and curvature for anyone dealing with methods for studying associations, comparing groups, or analyzing multivariate data
About the Author
Rand R. Wilcox has a Ph.D. in psychometrics, and is a professor of psychology at the University of Southern California. Wilcox's main research interests are statistical methods, particularly robust methods for comparing groups and studying associations. He also collaborates with researchers in occupational therapy, gerontology, biology, education and psychology. Wilcox is an internationally recognized expert in the field of Applied Statistics and has concentrated much of his research in the area of ANOVA and Regression. Wilcox is the author of 12 books on statistics and has published many papers on robust methods. He is currently an Associate Editor for four statistics journals and has served on many editorial boards. He has given numerous invited talks and workshops on robust methods.
"About this title" may belong to another edition of this title.
- PublisherAcademic Press
- Publication date2016
- ISBN 10 012804733X
- ISBN 13 9780128047330
- BindingHardcover
- LanguageEnglish
- Edition number4
- Number of pages810
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Introduction to Robust Estimation and Hypothesis Testing
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Elsevier Science & Technology, 2016
ISBN 10: 012804733X
ISBN 13: 9780128047330
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Introduction to Robust Estimation and Hypothesis Testing (Statistical Modeling and Decision Science)
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Introduction to Robust Estimation and Hypothesis Testing (Statistical Modeling and Decision Science)
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INTRODUCTION TO ROBUST ESTIMATION AND HYPOTHESIS TESTING
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Introduction to Robust Estimating and Hypothesis Testing, 4th Editon, is a 'how-to' on the application of robust methods using available software. Modern robust methods provide improved techniques for dealing with outliers, skewed distribution curvature and heteroscedasticity that can provide substantial gains in power as well as a deeper, more accurate and more nuanced understanding of data. Since the last edition, there have been numerous advances and improvements. They include new techniques for comparing groups and measuring effect size as well as new methods for comparing quantiles. Many new regression methods have been added that include both parametric and nonparametric techniques. The methods related to ANCOVA have been expanded considerably. New perspectives related to discrete distributions with a relatively small sample space are described as well as new results relevant to the shift function. The practical importance of these methods is illustrated using data from real world studies. The R package written for this book now contains over 1200 functions. New to this edition 35% revised content Covers many new and improved R functions New techniques that deal with a wide range of situations Extensive revisions to cover the latest developments in robust regression Covers latest improvements in ANOVA Includes newest rank-based methods Describes and illustrated easy to use software 810 pp. Englisch. Seller Inventory # 9780128047330
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Introduction to Robust Estimation and Hypothesis Testing (Statistical Modeling and Decision Science)
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