Nonparametric Statistical Inference

Jean Dickinson Gibbons is Russell Professor Emerita of Applied Statistics at the University of Alabama, where she also served as Chair for 20 years. She is a life member of the American Statistical Association, serving three terms on their Board of Directors; she was elected a Fellow in 1972. She earned the B.A. (1958) and M.A. (1959) in mathematics from Duke University and the Ph.D. (1962) in statistics from Virginia Tech, which recently named their graduate program after her. In addition to Alabama, she taught at the Wharton School of the University of Pennsylvania, the University of Cincinnati and Mercer University and offered short courses for the U.S. Army, the Naval Postgraduate School and the American Statistical Association. She currently lives in Vero Beach, Florida, where she is a peer leader in the Fielden Institute for Lifelong Learning at Indian River State College. Subhabrata Chakraborti is Professor of Statistics and Morrow Faculty Fellow at the University of Alabama. He is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. In 2019, he received the Burnum Distinguished Faculty Award and the SEC Professor of the Year Award at the University of Alabama. Professor Chakraborti has authored or co-authored over one hundred publications. He is the co-author of Nonparametric Statistical Process Control (2019) published by John Wiley. His research interests include applications of statistical methods, including nonparametric methods in industrial statistics and allied areas. Professor Chakraborti has been a Fulbright Scholar and a visiting professor in several countries including South Africa, India and Brazil; he is recognized for his work with students and scholars from around the world. He serves as an Associate Editor of Communications in Statistics and Quality Engineering.

Preface to the Sixth Edition. Author Biographies. 1. Introduction and Fundamentals. 2. Order Statistics, Quantiles, and Coverages. 3. Tests of Randomness. 4. Tests of Goodness of Fit. 5. One-Sample and Paired-Sample Procedures. 6. The General Two-Sample Problem. 7. Linear Rank Statistics and the General Two-Sample Problem. 8. Linear Rank Tests for the Location Problem. 9. Linear Rank Tests for the Scale Problem. 10. Tests of the Equality of k Distributions. 11. Measures of Association for Bivariate Samples. 12. Measures of Association in Multiple Classifications. 13. Asymptotic Relative Efficiency. 14. Analysis of Count Data. 15. Summary. Appendix of Tables. Answers to Selected Problems. References. Index.