Nonparametric Methods in Statistics with SAS Applications
Chapman and Hall/CRC – 2013 – 195 pages
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
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
Thin-Plate Smoothing Spline Method
Nonparametric Generalized Additive Regression
Nonparametric Binary Logistic Model
Nonparametric Poisson Model
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
Index of Notation
Exercises appear at the end of each chapter.
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.