Book Description
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 reallife 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 twosample location and dispersion problems, dichotomous data, and oneway and twoway 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 upperundergraduate and firstyear graduate courses in applied nonparametric statistics.
Table of Contents
 Cover
 Series
 Title Page
 Copyright
 Dedication
 Chapter 1: Introduction
 Chapter 2: The Dichotomous Data Problem

Chapter 3: The OneSample Location Problem
 Introduction
 Paired Replicates Analyses by Way of Signed Ranks
 3.1 A DistributionFree Signed Rank Test (WILCOXON)
 3.2 An Estimator Associated With Wilcoxon's Signed Rank Statistic (Hodges–Lehmann)
 3.3 A DistributionFree Confidence Interval Based on Wilcoxon's Signed Rank Test (TUKEY)
 Paired Replicates Analyses by Way of Signs
 3.4 A DistributionFree Sign Test (Fisher)
 3.5 An Estimator Associated with The Sign Statistic (Hodges–Lehmann)
 3.6 A DistributionFree Confidence Interval Based on the Sign Test (Thompson, Savur)
 OneSample Data*
 3.7 Procedures Based on the Signed Rank Statistic
 3.8 Procedures Based on the Sign Statistic
 3.9 An Asymptotically DistributionFree Test of Symmetry (Randles–Fligner– Policello–Wolfe, Davis–Quade)
 Bivariate Data
 3.10 A DistributionFree Test for Bivariate Symmetry (Hollander)
 Hypothesis
 3.11 Efficiencies of Paired Replicates and OneSample Location Procedures

Chapter 4: The TwoSample Location Problem
 Introduction
 4.1 A DistributionFree Rank Sum Test (Wilcoxon, Mann and Whitney)
 4.2 An Estimator Associated with Wilcoxon's Rank Sum Statistic (Hodges–Lehmann)
 4.3 A DistributionFree Confidence Interval Based on Wilcoxon's Rank Sum Test (Moses)
 4.4 A Robust Rank Test for the Behrens–Fisher Problem (Fligner–Policello)
 4.5 Efficiencies of TwoSample Location Procedures

Chapter 5: The TwoSample Dispersion Problem and Other TwoSample Problems
 Introduction
 5.1 A DistributionFree Rank Test for Dispersion—Medians Equal (Ansari–Bradley)
 5.2 An Asymptotically DistributionFree Test for Dispersion Based on the Jackknife–Medians not Necessarily Equal (Miller)
 5.3 A DistributionFree Rank Test for Either Location or Dispersion (Lepage)
 5.4 A DistributionFree Test for General Differences in Two Populations (Kolmogorov–Smirnov)
 5.5 Efficiencies of TwoSample Dispersion and Broad Alternatives Procedures

Chapter 6: The OneWay Layout
 Introduction
 6.1 A DistributionFree Test for General Alternatives (Kruskal–Wallis)
 6.2 A DistributionFree Test for Ordered Alternatives (Jonckheere–Terpstra)
 6.3 DistributionFree Tests for Umbrella Alternatives (Mack–Wolfe)
 6.3A A DistributionFree Test for Umbrella Alternatives, Peak Known (Mack–Wolfe)
 6.3B A DistributionFree Test for Umbrella Alternatives, Peak Unknown (Mack–Wolfe)
 6.4 A DistributionFree Test for Treatments Versus a Control (Fligner–Wolfe)
 Rationale For Multiple Comparison Procedures
 6.5 DistributionFree TwoSided AllTreatments Multiple Comparisons Based on Pairwise Rankings—General Configuration (Dwass, Steel, and Critchlow–Fligner)
 6.6 DistributionFree OneSided AllTreatments Multiple Comparisons Based on Pairwise RankingsOrdered Treatment Effects (Hayter–Stone)
 6.7 DistributionFree OneSided Treatments VersusControl Multiple Comparisons Based on Joint Rankings (Nemenyi, Damico–Wolfe)
 6.8 Contrast Estimation Based on Hodges–Lehmann TwoSample Estimators (Spjøtvoll)
 6.9 Simultaneous Confidence Intervals for All Simple Contrasts (Critchlow–Fligner)
 6.10 Efficiencies of OneWay Layout Procedures

Chapter 7: The TwoWay Layout
 Introduction
 7.1 A DistributionFree Test For General Alternatives In A Randomized Complete Block Design (Friedman, KendallBabington Smith)
 7.2 A DistributionFree Test for Ordered Alternatives in a Randomized Complete Block Design (Page)
 Rationale for Multiple Comparison Procedures
 7.3 DistributionFree TwoSided AllTreatments Multiple Comparisons Based on Friedman Rank Sums—General Configuration (Wilcoxon, Nemenyi, McdonaldThompson)
 7.4 DistributionFree OneSided Treatments Versus Control Multiple Comparisons Based On Friedman Rank Sums (Nemenyi, Wilcoxon–Wilcox, Miller)
 7.5 Contrast Estimation Based on OneSample Median Estimators (Doksum)
 Incomplete Block Data—TwoWay Layout With Zero or One Observation Per Treatment–Block Combination
 7.6 A DistributionFree Test for General Alternatives In a Randomized Balanced Incomplete Block Design (Bibd) (Durbin–Skillings–Mack)
 7.7 Asymptotically DistributionFree TwoSided AllTreatments Multiple Comparisons for Balanced Incomplete Block Designs (Skillings–Mack)
 7.8 A DistributionFree Test for General Alternatives for Data From an Arbitrary Incomplete Block Design (Skillings–Mack)
 Replications—TwoWay Layout With at Least One Observation for Every Treatment–Block Combination
 7.9 A DistributionFree Test for General Alternatives In a Randomized Block Design With an Equal Number c(>1) of Replications Per Treatment–Block Combination (Mack–Skillings)
 7.10 Asymptotically DistributionFree TwoSided AllTreatments Multiple Comparisons for a TwoWay Layout With an Equal Number of Replications In Each Treatment–Block Combination (Mack–Skillings)
 Analyses Associated With Signed Ranks
 7.11 A Test Based on Wilcoxon Signed Ranks for General Alternatives in A Randomized Complete Block Design (Doksum)
 7.12 A Test Based on Wilcoxon Signed Ranks for Ordered Alternatives in a Randomized Complete Block Design (Hollander)
 7.13 Approximate TwoSided AllTreatments Multiple Comparisons Based on Signed Ranks (Nemenyi)
 7.14 Approximate OneSided TreatmentsVersusControl Multiple Comparisons Based On Signed Ranks (Hollander)
 7.15 Contrast Estimation Based on the OneSample Hodges–Lehmann Estimators (Lehmann)
 7.16 Efficiencies of TwoWay Layout Procedures

Chapter 8: The Independence Problem
 Introduction
 8.1 A DistributionFree Test for Independence Based on Signs (Kendall)
 8.2 An Estimator Associated With The Kendall Statistic (Kendall)
 8.3 An Asymptotically Distribution–Free Confidence Interval Based On The Kendall Statistic (Samara–Randles, Fligner–Rust, Noether)
 8.4 An Asymptotically DistributionFree Confidence Interval Based On Efron's Bootstrap
 8.5 A DistributionFree Test for Independence Based on Ranks (Spearman)
 8.6 A DistributionFree Test for Independence Against Broad Alternatives (Hoeffding)
 8.7 Efficiencies of Independence Procedures

Chapter 9: Regression Problems
 Introduction
 One Regression Line
 9.1 A DistributionFree Test for the Slope of the Regression Line (Theil)
 9.2 A Slope Estimator Associated with the Theil Statistic (Theil)
 9.3 A DistributionFree Confidence Interval Associated with the Theil Test (Theil)
 9.4 An Intercept Estimator Associated with the Theil Statistic and Use of the Estimated Linear Relationship for Prediction (Hettmansperger–Mckean–Sheather)
 k(≥2) Regression Lines
 9.5 An Asymptotically DistributionFree Test for the Parallelism of Several Regression Lines (Sen, Adichie)
 General Multiple Linear Regression
 9.6 Asymptotically DistributionFree RankBased Tests for General Multiple Linear Regression (Jaeckel, Hettmansperger–Mckean)
 Nonparametric Regression Analysis
 9.7 An Introduction to NonRankBased Approaches to Nonparametric Regression Analysis
 9.8 Efficiencies of Regression Procedures

Chapter 10: Comparing Two Success Probabilities
 Introduction
 10.1 Approximate Tests and Confidence Intervals for The Difference Between Two Success Probabilities (Pearson)
 10.2 An Exact Test for the Difference Between Two Success Probabilities (Fisher)
 10.3 Inference for the Odds Ratio (Fisher, Cornfield)
 10.4 Inference for k Strata of 2×2 Tables (Mantel and Haenszel)
 10.5 Efficiencies

Chapter 11: Life Distributions and Survival Analysis
 Introduction
 11.1 A Test of Exponentiality Versus IFR Alternatives (Epstein)
 11.2 A Test of Exponentiality Versus NBU Alternatives (Hollander–Proschan)
 11.3 A Test of Exponentiality Versus DMRL Alternatives (Hollander–Proschan)
 11.4 A Test of Exponentiality Versus a Trend Change in Mean Residual Life (Guess–Hollander–Proschan)
 11.5 A Confidence Band for the Distribution Function (Kolmogorov)
 11.6 An Estimator of the Distribution Function When the Data are Censored (Kaplan–Meier)
 11.7 A TwoSample Test for Censored Data (Mantel)
 11.8 Efficiencies
 Chapter 12: Density Estimation
 Chapter 13: Wavelets
 Chapter 14: Smoothing

Chapter 15: Ranked Set Sampling
 Introduction
 15.1 Rationale and Historical Development
 15.2 Collecting a Ranked Set Sample
 15.3 Ranked Set Sampling Estimation of a Population Mean
 15.4 Ranked Set Sample Analogs of the Mann–Whitney–Wilcoxon TwoSample Procedures (Bohn–Wolfe)
 15.5 Other Important Issues for Ranked Set Sampling
 15.6 Extensions and Related Approaches
 Chapter 16: An Introduction to Bayesian Nonparametric Statistics via the Dirichlet Process
 Bibliography
 R Program Index
 Author Index
 Subject Index
 Series
Product Information
 Title: Nonparametric Statistical Methods, 3rd Edition
 Author(s):
 Release date: November 2013
 Publisher(s): Wiley
 ISBN: 9781118553299