Statistical Methods for Ranking Data
Language: English
Published by Springer New York, 2014
Series: Book 2 of 6 - Frontiers in Probability and the Statistical Sciences
- Hardcover
- New
Seller: moluna, Greven, Germanymoluna
AbeBooks seller since July 9, 2020
Condition: New
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Add to basketItem description from seller
Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Contains a unified treatment of both inference and modeling for ranking dataContains comprehensive software to enable the practitioner to access the methodsContains illustrative data sets and exercises so that it can be used as a textbook i.
Seller Inventory # 4214137
- Title
- Statistical Methods for Ranking Data
- Author
- Mayer Alvo|Philip L.H. Yu
- Publisher
- Springer New York
- Publication year
- 2014
- Condition
- New
- Binding
- Gebunden
- 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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