Statistical Methods for Ranking Data
Language: English
Published by Springer, 2014
Series: Book 2 of 6 - Frontiers in Probability and the Statistical Sciences
- Hardcover
- Used
Seller: Ammareal, Morangis, FranceAmmareal
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Condition: Used - Fine
Current price: US$ 23.39
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Ancien livre de bibliothèque avec équipements. Edition 2014. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 2014. Ammareal gives back up to 15% of this item's net price to charity organizations.
Seller Inventory # H-108-897
- Title
- Statistical Methods for Ranking Data
- Author
- Mayer Alvo et Philip L.H. Yu
- Publisher
- Springer
- Publication year
- 2014
- Condition
- Très bon
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1493914707
- ISBN 13
- 9781493914708
- Series
- Book 2 of 6: Frontiers in Probability and the Statistical Sciences
This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis.
This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors’ website.
"Synopsis" may belong to another edition of this title.
From the Back Cover
This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis.
This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors’ website.
"About the title" may belong to another edition of this title.
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