Nonparametric Statistical Methods
Myles Hollander, Douglas A. Wolfe, Eric Chicken
- English
- PDF
- Disponibile su iOS e Android
Nonparametric Statistical Methods
Myles Hollander, Douglas A. Wolfe, Eric Chicken
Informazioni sul libro
Praise for the Second Edition
"This book should be an essential part of the personal library of every practicing statistician." — Technometrics
Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation.
Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features:
- The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition
- New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics
- Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science
Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.