A Parametric Approach to Nonparametric Statistics

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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.

Book details

Edition:
1st ed. 2018
Series:
Springer Series in the Data Sciences
Author:
Mayer Alvo, Philip L. Yu
ISBN:
9783319941530
Related ISBNs:
9783319941523
Publisher:
Springer International Publishing
Pages:
N/A
Reading age:
Not specified
Includes images:
Yes
Date of addition:
2018-10-21
Usage restrictions:
Copyright
Copyright date:
2018
Copyright by:
Springer Nature Switzerland AG 
Adult content:
No
Language:
English
Categories:
Mathematics and Statistics, Nonfiction