Textbook
Nonparametric Statistical Methods, 3rd EditionISBN: 9780470387375
848 pages
December 2013, ©2014

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
Table of Contents
Preface xiii
1. Introduction 1
1.1. Advantages of Nonparametric Methods 1
1.2. The DistributionFree Property 2
1.3. Some RealWorld Applications 3
1.4. Format and Organization 6
1.5. Computing with R 8
1.6. Historical Background 9
2. The Dichotomous Data Problem 11
Introduction 11
2.1. A Binomial Test 11
2.2. An Estimator for the Probability of Success 22
2.3. A Confidence Interval for the Probability of Success (Wilson) 24
2.4. Bayes Estimators for the Probability of Success 33
3. The OneSample Location Problem 39
Introduction 39
Paired Replicates Analyses by Way of Signed Ranks 39
3.1. A DistributionFree Signed Rank Test (Wilcoxon) 40
3.2. An Estimator Associated with Wilcoxon’s Signed Rank Statistic (Hodges–Lehmann) 56
3.3. A DistributionFree Confidence Interval Based on Wilcoxon’s Signed Rank Test (Tukey) 59
Paired Replicates Analyses by Way of Signs 63
3.4. A DistributionFree Sign Test (Fisher) 63
3.5. An Estimator Associated with the Sign Statistic (Hodges–Lehmann) 76
3.6. A DistributionFree Confidence Interval Based on the Sign Test (Thompson, Savur) 80
OneSample Data 84
3.7. Procedures Based on the Signed Rank Statistic 84
3.8. Procedures Based on the Sign Statistic 90
3.9. An Asymptotically DistributionFree Test of Symmetry (Randles–Fligner–Policello–Wolfe, Davis–Quade) 94
Bivariate Data 102
3.10. A DistributionFree Test for Bivariate Symmetry (Hollander) 102
3.11. Efficiencies of Paired Replicates and OneSample Location Procedures 112
4. The TwoSample Location Problem 115
Introduction 115
4.1. A DistributionFree Rank Sum Test (Wilcoxon, Mann and Whitney) 115
4.2. An Estimator Associated with Wilcoxon’s Rank Sum Statistic (Hodges–Lehmann) 136
4.3. A DistributionFree Confidence Interval Based on Wilcoxon’s Rank Sum Test (Moses) 142
4.4. A Robust Rank Test for the Behrens–Fisher Problem (Fligner–Policello) 145
4.5. Efficiencies of TwoSample Location Procedures 149
5. The TwoSample Dispersion Problem and Other TwoSample Problems 151
Introduction 151
5.1. A DistributionFree Rank Test for Dispersion–Medians Equal (Ansari–Bradley) 152
5.2. An Asymptotically DistributionFree Test for Dispersion Based on the Jackknife–Medians Not Necessarily Equal (Miller) 169
5.3. A DistributionFree Rank Test for Either Location or Dispersion (Lepage) 181
5.4. A DistributionFree Test for General Differences in Two Populations (Kolmogorov–Smirnov) 190
5.5. Efficiencies of TwoSample Dispersion and Broad Alternatives Procedures 200
6. The OneWay Layout 202
Introduction 202
6.1. A DistributionFree Test for General Alternatives (Kruskal–Wallis) 204
6.2. A DistributionFree Test for Ordered Alternatives (Jonckheere–Terpstra) 215
6.3. DistributionFree Tests for Umbrella Alternatives (Mack–Wolfe) 225
6.3A. A DistributionFree Test for Umbrella Alternatives, Peak Known (Mack–Wolfe) 226
6.3B. A DistributionFree Test for Umbrella Alternatives, Peak Unknown (Mack–Wolfe) 241
6.4. A DistributionFree Test for Treatments Versus a Control (Fligner–Wolfe) 249
Rationale For Multiple Comparison Procedures 255
6.5. DistributionFree TwoSided AllTreatments Multiple Comparisons Based on Pairwise Rankings–General Configuration (Dwass, Steel, and Critchlow–Fligner) 256
6.6. DistributionFree OneSided AllTreatments Multiple Comparisons Based on Pairwise RankingsOrdered Treatment Effects (Hayter–Stone) 265
6.7. DistributionFree OneSided TreatmentsVersusControl Multiple Comparisons Based on Joint Rankings (Nemenyi, Damico–Wolfe) 271
6.8. Contrast Estimation Based on Hodges–Lehmann TwoSample Estimators (Spjøtvoll) 278
6.9. Simultaneous Confidence Intervals for All Simple Contrasts (Critchlow–Fligner) 282
6.10. Efficiencies of OneWay Layout Procedures 287
7. The TwoWay Layout 289
Introduction 289
7.1. A DistributionFree Test for General Alternatives in a Randomized Complete Block Design (Friedman, KendallBabington Smith) 292
7.2. A DistributionFree Test for Ordered Alternatives in a Randomized Complete Block Design (Page) 304
Rationale for Multiple Comparison Procedures 315
7.3. DistributionFree TwoSided AllTreatments Multiple Comparisons Based on Friedman Rank Sums–General Configuration (Wilcoxon, Nemenyi, McDonaldThompson) 316
7.4. DistributionFree OneSided Treatments Versus Control Multiple Comparisons Based on Friedman Rank Sums (Nemenyi, WilcoxonWilcox, Miller) 322
7.5. Contrast Estimation Based on OneSample Median Estimators (Doksum) 328
Incomplete Block Data–TwoWay Layout with Zero or One Observation Per Treatment–Block Combination 331
7.6. A DistributionFree Test for General Alternatives in a Randomized Balanced Incomplete Block Design (BIBD) (Durbin–Skillings–Mack) 332
7.7. Asymptotically DistributionFree TwoSided AllTreatments Multiple Comparisons for Balanced Incomplete Block Designs (Skillings–Mack) 341
7.8. A DistributionFree Test for General Alternatives for Data From an Arbitrary Incomplete Block Design (Skillings–Mack) 343
Replications–TwoWay Layout with at Least One Observation for Every Treatment–Block Combination 354
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) 354
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) 367
Analyses Associated with Signed Ranks 370
7.11. A Test Based on Wilcoxon Signed Ranks for General Alternatives in a Randomized Complete Block Design (Doksum) 370
7.12. A Test Based on Wilcoxon Signed Ranks for Ordered Alternatives in a Randomized Complete Block Design (Hollander) 376
7.13. Approximate TwoSided AllTreatments Multiple Comparisons Based on Signed Ranks (Nemenyi) 379
7.14. Approximate OneSided TreatmentsVersusControl Multiple Comparisons Based on Signed Ranks (Hollander) 382
7.15. Contrast Estimation Based on the OneSample Hodges–Lehmann Estimators (Lehmann) 386
7.16. Efficiencies of TwoWay Layout Procedures 390
8. The Independence Problem 393
Introduction 393
8.1. A DistributionFree Test for Independence Based on Signs (Kendall) 393
8.2. An Estimator Associated with the Kendall Statistic (Kendall) 413
8.3. An Asymptotically DistributionFree Confidence Interval Based on the Kendall Statistic (SamaraRandles, Fligner–Rust, Noether) 415
8.4. An Asymptotically DistributionFree Confidence Interval Based on Efron’s Bootstrap 420
8.5. A DistributionFree Test for Independence Based on Ranks (Spearman) 427
8.6. A DistributionFree Test for Independence Against Broad Alternatives (Hoeffding) 442
8.7. Efficiencies of Independence Procedures 450
9. Regression Problems 451
Introduction 451
One Regression Line 452
9.1. A DistributionFree Test for the Slope of the Regression Line (Theil) 452
9.2. A Slope Estimator Associated with the Theil Statistic (Theil) 458
9.3. A DistributionFree Confidence Interval Associated with the Theil Test (Theil) 460
9.4. An Intercept Estimator Associated with the Theil Statistic and Use of the Estimated Linear Relationship for Prediction (Hettmansperger–McKean–Sheather) 463
k(≥2) Regression Lines 466
9.5. An Asymptotically DistributionFree Test for the Parallelism of Several Regression Lines (Sen, Adichie) 466
General Multiple Linear Regression 475
9.6. Asymptotically DistributionFree RankBased Tests for General Multiple Linear Regression (Jaeckel, Hettmansperger–McKean) 475
Nonparametric Regression Analysis 490
9.7. An Introduction to NonRankBased Approaches to Nonparametric Regression Analysis 490
9.8. Efficiencies of Regression Procedures 494
10. Comparing Two Success Probabilities 495
Introduction 495
10.1. Approximate Tests and Confidence Intervals for the Difference between Two Success Probabilities (Pearson) 496
10.2. An Exact Test for the Difference between Two Success Probabilities (Fisher) 511
10.3. Inference for the Odds Ratio (Fisher, Cornfield) 515
10.4. Inference for k Strata of 2 × 2 Tables (Mantel and Haenszel) 522
10.5. Efficiencies 534
11. Life Distributions and Survival Analysis 535
Introduction 535
11.1. A Test of Exponentiality Versus IFR Alternatives (Epstein) 536
11.2. A Test of Exponentiality Versus NBU Alternatives (Hollander–Proschan) 545
11.3. A Test of Exponentiality Versus DMRL Alternatives (Hollander–Proschan) 555
11.4. A Test of Exponentiality Versus a Trend Change in Mean Residual Life (Guess–Hollander–Proschan) 563
11.5. A Confidence Band for the Distribution Function (Kolmogorov) 568
11.6. An Estimator of the Distribution Function When the Data are Censored (Kaplan–Meier) 578
11.7. A TwoSample Test for Censored Data (Mantel) 594
11.8. Efficiencies 605
12. Density Estimation 609
Introduction 609
12.1. Density Functions and Histograms 609
12.2. Kernel Density Estimation 617
12.3. Bandwidth Selection 624
12.4. Other Methods 628
13. Wavelets 629
Introduction 629
13.1. Wavelet Representation of a Function 630
13.2. Wavelet Thresholding 644
13.3. Other Uses of Wavelets in Statistics 655
14. Smoothing 656
Introduction 656
14.1. Local Averaging (Friedman) 657
14.2. Local Regression (Cleveland) 662
14.3. Kernel Smoothing 667
14.4. Other Methods of Smoothing 675
15. Ranked Set Sampling 676
Introduction 676
15.1. Rationale and Historical Development 676
15.2. Collecting a Ranked Set Sample 677
15.3. Ranked Set Sampling Estimation of a Population Mean 685
15.4. Ranked Set Sample Analogs of the Mann–Whitney–Wilcoxon TwoSample Procedures (Bohn–Wolfe) 717
15.5. Other Important Issues for Ranked Set Sampling 737
15.6. Extensions and Related Approaches 742
16. An Introduction to Bayesian Nonparametric Statistics via the Dirichlet Process 744
Introduction 744
16.1. Ferguson’s Dirichlet Process 745
16.2. A Bayes Estimator of the Distribution Function (Ferguson) 749
16.3. Rank Order Estimation (Campbell and Hollander) 752
16.4. A Bayes Estimator of the Distribution When the Data are RightCensored (Susarla and Van Ryzin) 755
16.5. Other Bayesian Approaches 759
Bibliography 763
R Program Index 791
Author Index 799
Subject Index 809
Author Information
MYLES HOLLANDER is Robert O. Lawton Distinguished Professor of Statistics and Professor Emeritus at the Florida State University in Tallahassee. He served as editor of the Theory and Methods Section of the Journal of the American Statistical Association, 1993–96, and he received the Gottfried E. Noether Senior Scholar Award from the American Statistical Association in 2003.
DOUGLAS A. WOLFE is Professor and Chair Emeritus in the Department of Statistics at Ohio State University in Columbus. He is a twotime recipient of the Ohio State University Alumni Distinguished Teaching Award, in 1973–74 and 1988–89.
ERIC CHICKEN is Associate Professor at the Florida State University in Tallahassee. He is active in modern nonparametric statistics research fields, including functional analysis, sequential methods, and complex system applications.