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

Alvo, Mayer; Yu, Philip L. H.

Softcover

ISBN 10: 3030068048 ISBN 13: 9783030068042

Publisher: Springer, 2018

Synopsis

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.

About this title

About the Author

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.

From the Back Cover

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.

Edition details

• Publisher: Springer
• Publication date: 2018
• Language: English
• ISBN 10: 3030068048
• ISBN 13: 9783030068042
• Binding: Paperback
• Number of pages: 293