Statistical Methods for Ranking Data - Couverture souple

Alvo, Mayer; Yu, Philip L. H.

Couverture souple

ISBN 10 : 1493947818 ISBN 13 : 9781493947812

Editeur : Springer, 2016

Synopsis

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.

A propos du livre

À propos de l'auteur

Mayer Alvo is a Professor in the Department of Mathematics and Statistics at the University of Ottawa. He received his Ph.D. from Columbia University in 1972. He served as Departmental Chairman in 1985-88, 2002- 2005 and 2011-2012. He is the author of more than 64 articles published in refereed journals. His research interests include nonparametric statistics, Bayesian analysis and sequential methods.

Philip L.H. Yu is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Hong Kong. He received his Ph.D. from The University of Hong Kong in 1993. He is the Director of the Master of Statistics Programme. He is an Associate Editor for Computational Statistics and Data Analysis as well as for Computational Statistics. He is the author of more than 90 referred publications. His research interests include modeling of ranking data, data mining and financial and risk analytics.

À propos de la quatrième de couverture

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.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2016
• Langue: anglais
• ISBN 10: 1493947818
• ISBN 13: 9781493947812
• Reliure: Broché
• Nombre de pages: 273
• Coordonnées du fabricant: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne