Nonparametric Methods in Statistics with SAS Applications

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.

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 SamplesKruskal-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 Definition 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 Histogram Kernel Density Estimator Resampling Methods for Interval Estimation Jackknife Bootstrap Appendix A: Tables Appendix B: Answers to Exercises Recommended Books Index of Notation Index 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.