Nonparametric Statistical Methods, 3rd Edition

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.

Table of Contents

Chapter 1 Introduction
Chapter 2 The Dichotomous Data Problem
Chapter 3 The One-Sample Location Problem
Chapter 4 The Two-Sample Location Problem
Chapter 5 The Two-Sample Dispersion Problem and Other Two-Sample Problems
Chapter 6 The One-Way Layout
Chapter 7 The Two-Way Layout
Chapter 8 The Independence Problem
Chapter 9 Regression Problems
Chapter 10 Comparing Two Success Probabilities
Chapter 11 Life Distributions and Survival Analysis
Chapter 12 Density Estimation
Chapter 13 Wavelets
Chapter 14 Smoothing
Chapter 15 Ranked Set Sampling
Chapter 16 An Introduction to Bayesian Nonparametric Statistics via the Dirichlet Process