Parametric Approach to Nonparametric Statistics

Mayer Alvo is a Professor in the Department of Mathematics and Statistics at the University of Ottawa. He received his Ph.D. from Columbia University in 1972. He served as Departmental Chairman in 1985-88, 2002- 2005 and 2011-2012. He is the author of more than 64 articles published in refereed journals. His research interests include nonparametric statistics, Bayesian analysis and sequential methods. Philip L.H. Yu is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Hong Kong. He received his Ph.D. from The University of Hong Kong in 1993. He is the Director of the Master of Statistics Programme. He is an Associate Editor for Computational Statistics and Data Analysis as well as for Computational Statistics. He is the author of more than 90 referred publications.  His research interests include modeling of ranking data, data mining and financial and risk analytics.

“The book is interesting and well written. Theoretical results and formulas derived are illustrated by various numerical examples. The majority of chapters are equipped with interesting exercises for the readers.” (Jonas Šiaulys, zbMath 1416.62006, 2019)

• I. Introduction and Fundamentals.- Introduction.- Fundamental Concepts in Parametric Inference.- II. Modern Nonparametric Statistical Methods.- Smooth Goodness of Fit Tests.- One-Sample and Two-Sample Problems.- Multi-Sample Problems.- Tests for Trend and Association.- Optimal Rank Tests.- Efficiency.- III. Selected Applications.- Multiple Change-Point Problems.- Bayesian Models for Ranking Data.- Analysis of Censored Data.- A. Description of Data Sets.