Nonparametric Methods in Statistics with SAS Applications: 1st Edition (Paperback) book cover

Nonparametric Methods in Statistics with SAS Applications

1st Edition

By Olga Korosteleva

Chapman and Hall/CRC

195 pages | 22 B/W Illus.

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Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods.

The text begins with classical nonparametric hypotheses testing, including the sign, Wilcoxon sign-rank and rank-sum, Ansari-Bradley, Kolmogorov-Smirnov, Friedman rank, Kruskal-Wallis H, Spearman rank correlation coefficient, and Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as binary logistic and Poisson models. The author also describes time-to-event nonparametric estimation methods, such as the Kaplan-Meier survival curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics of jackknife and bootstrap interval estimation.

Drawing on data sets from the author’s many consulting projects, this classroom-tested book includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author’s website.


"… just what I have been looking for. The purpose of the book is to teach master’s students applications of popular nonparametric methods making use of SAS 9.3 software. … My favorite feature of this book is that it has many examples and exercises. The examples are carefully chosen from various areas such as education, psychology, and clinical trials. Each chapter contains a collection of exercises with datasets and the larger ones can be downloaded from author’s book website. The author also offers a solution manual for all exercises … a great reference for students and practitioners who are interested in using SAS to apply nonparametric methods."

The American Statistician, February 2015

Table of Contents

Hypotheses Testing for Two Samples

Sign Test for Location Parameter for Matched Paired Samples

Wilcoxon Signed-Rank Test for Location Parameter for Matched Paired Samples

Wilcoxon Rank-Sum Test for Location Parameter for Two Independent Samples

Ansari-Bradley Test for Scale Parameter for Two Independent Samples

Kolmogorov-Smirnov Test for Equality of Distributions

Hypotheses Testing for Several Samples

Friedman Rank Test for Location Parameter for Several Dependent Samples

Kruskal-Wallis H-Test for Location Parameter for Several Independent Samples

Tests for Categorical Data

Spearman Rank Correlation Coefficient Test

Fisher Exact Test

Nonparametric Regression

Loess Regression

Thin-Plate Smoothing Spline Method

Nonparametric Generalized Additive Regression


Nonparametric Binary Logistic Model

Nonparametric Poisson Model

Time-to-Event Analysis

Kaplan-Meier Estimator of Survival Function

Log-Rank Test for Comparison of Two Survival Functions

Cox Proportional Hazards Model

Univariate Probability Density Estimation


Kernel Density Estimator

Resampling Methods for Interval Estimation



Appendix A: Tables

Appendix B: Answers to Exercises

Recommended Books

Index of Notation


Exercises appear at the end of each chapter.

About the Author

Olga Korosteleva is an associate professor of statistics in the Department of Mathematics and Statistics at California State University, Long Beach (CSULB). She received a Ph.D. in statistics from Purdue University.

About the Series

Chapman & Hall/CRC Texts in Statistical Science

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General