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A Parametric Approach to Nonparametric Statistics de Mayer Alvo

A Parametric Approach to Nonparametric Statistics: Springer Series in the Data Sciences

Autor Mayer Alvo, Philip L. H. Yu
en Limba Engleză Hardback – 23 oct 2018
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.
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Specificații

ISBN-13: 9783319941523
ISBN-10: 3319941526
Pagini: 268
Ilustrații: XIV, 279 p. 15 illus. in color.
Dimensiuni: 210 x 279 x 20 mm
Greutate: 1.04 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria Springer Series in the Data Sciences

Locul publicării:Cham, Switzerland

Cuprins

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.

Recenzii

“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)

Notă biografică

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.

Textul de pe ultima copertă

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.

Caracteristici

Includes exercises at the end of every chapter First book to bridge the gap between parametric and nonparametric statistics Contains introduction to probability and statistics Demonstrates Modern applications