Springer the Data Sciences

By: Mayer Alvo, Philip L. H. Yu

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Paperback | 20 December 2018

At a Glance

Paperback
296 Pages

Dimensions(cm)
27.89 x 20.98 x 1.58

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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.

Industry Reviews

"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 Å iaulys, zbMath 1416.62006, 2019)

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