A Parametric Approach to Nonparametric Statistics (Springer Series in the Data Sciences) - Softcover

Über die Autorin bzw. den Autor

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. 

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This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data. The book bridges the gap between parametric and nonparametric statistics and presents the best practices of the former while enjoying the robustness properties of the latter.

This book can be used in a graduate course in nonparametrics, with parts being accessible to senior undergraduates.  In addition, the book will be of wide interest to statisticians and researchers in applied fields.