A Parametric Approach to Nonparametric Statistics (Springer Series in the Data Sciences)
Language: English
Published by Springer International Publishing, 2018
- Hardcover
- Used
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: Used - Fine
£ 37.41
Quantity: 1 available
Add to basketItem description from seller
Gebraucht - Sehr gut SG - leichte Beschädigungen oder Verschmutzungen, ungelesenes Mängelexemplar, gestempelt - 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.
Seller Inventory # INF1000547460
- Title
- A Parametric Approach to Nonparametric Statistics (Springer Series in the Data Sciences)
- Author
- Mayer Alvo, Philip L. H. Yu
- Publisher
- Springer International Publishing
- Publication year
- 2018
- Condition
- Sehr gut
- Binding
- Gebundene Ausgabe
- Language
- English
- ISBN 10
- 3319941526
- ISBN 13
- 9783319941523
- Item weight
- 981 grams
- Dimensions
- 285x215x22 mm
- Series
- Book 2 of 11: Springer Series in the Data Sciences
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.
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
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