Applied Nonparametric Statistical Methods

… The greatest strength of this book is that it is written at a level that is perfectly understandable by readers with only a course or two of introductory-level statistics. As such, it is appropriate for use as either a textbook for a first course in nonparametric methods for undergraduate statistics majors or as a reference for practitioners in other fields. It is also quite suitable as a supplementary statistics textbook for graduate students … . Key concepts are taught using worked-out examples from a variety of fields. … a worthwhile choice for either an introductory-level textbook or a self-study reference for nonspecialists. The writing is very accessible and not weighted down by any mathematics beyond the grasp of the intended audience. …—Psychometrika, Vol. 75, No. 3, September 2010… this book has an effective organization and covers a wider scope of non-parametric methods than former editions. Therefore, I believe that this book can serve its intended audience.—Journal of the Royal Statistical Society, Series A, Vol. 173, Issue 1, January 2010Most fourth editions look surprisingly similar to the third editions. Applied Nonparametric Statistical Methods is an exception. Sprent and Smeeton have taken an accessible and well-regarded work and expanded, reorganized, and improved on it. … Sprent and Smeeton offer a strong connection with respect to the how and why of the techniques. … The book’s major strength is its prioritization of coverage. The authors take painstaking care to inculcate an understanding of the appropriate use of nonparametric methods, as well as an appreciation for their application over a wide range of fields. The examples are well chosen, and the variety should ensure that every reader finds at least some of the problems interesting. … As a competitor to the texts by Conover (1999), Gibbons and Chakraborti (2004), Higgins (2004), and Wasserman (2006), Applied Nonparametric Statistical Methods more than holds its own. The combination of clear writing and comprehensive coverage make it an excellent introductory text. …—Technometrics, Vol. 51, No. 2, May 2009…The chapters have been substantially reorganized, and new material is provided on methods related to factorial designs and time-to-event data. An entirely new chapter, ‘Modern Nonparametrics,’ closes the text with a variety of topics … the worked examples are thoroughly and meticulously done … constant mention is made of the available software (e.g., StatXact, R, Minitab, SPSS) to conduct specific procedures. … solutions to selected end-of-chapter exercises are annotated and quite helpful. Overall, this is a solid choice for a first course in nonparametric statistics for undergraduates.—Journal of the American Statistical Association, Vol. 104, No. 487, September 2009… expands coverage on the analysis of survival data and the bootstrap method. … the new edition also focuses on some modern developments. The formal testing procedures are illustrated in a nice way with realistic examples leading to final conclusions, comments, and a discussion… The book has a clear style with well-organized material. The book works well as a reference book for users of nonparametric methods in different research areas. It is also a good textbook for undergraduate courses in statistics as well as courses for students majoring in other disciplines.—Hannu Oja, International Statistical Review, Vol. 27, No. 1, 2008 Praise for the Third EditionStrengths of this text certainly include its organization and writing style. Applied Nonparametric Statistical Methods provides a very clear exposition of modern nonparametric methods. Many students and practitioners will find it an excellent resource and reference for nonparametric statistics.—Technometrics, 2003… extremely valuable for statisticians as well as for researchers in applied fields. … This well-written book is highly recommended for those readers who want to get a feeling for the nonparametric methods which they apply when analysing their data.—Statistics in Medicine, 2004

• Some basic concepts. Fundamentals of nonparametric methods. Location inference for single samples. Other single-sample inferences. Methods for paired samples. Methods for two independent samples. Basic tests for three or more samples. Analysis of structured data. Analysis of survival data. Correlation and concordance. Bivariate linear regression. Categorical data. Association in categorical data. Robust estimation. Modern nonparametrics.