Statistical Methods for Ranking Data (Frontiers in Probability and the Statistical Sciences)
Language: English
Published by Springer, 2016
Series: Book 2 of 6 - Frontiers in Probability and the Statistical Sciences
- Softcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
AbeBooks seller since March 25, 2015
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In English.
Seller Inventory # ria9781493947812_new
- Title
- Statistical Methods for Ranking Data (Frontiers in Probability and the Statistical Sciences)
- Author
- Alvo, Mayer; Yu, Philip L.H.
- Publisher
- Springer
- Publication year
- 2016
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1493947818
- ISBN 13
- 9781493947812
- Item weight
- 528 grams
- 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.
Ria Christie Collections
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