Statistical Methods for Ranking Data

Author:   Mayer Alvo ,  Philip L.H. Yu
Publisher:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2014
ISBN:  

9781493947812


Pages:   273
Publication Date:   17 September 2016
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Statistical Methods for Ranking Data


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Author:   Mayer Alvo ,  Philip L.H. Yu
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2014
Dimensions:   Width: 15.50cm , Height: 1.50cm , Length: 23.50cm
Weight:   4.394kg
ISBN:  

9781493947812


ISBN 10:   1493947818
Pages:   273
Publication Date:   17 September 2016
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Reviews

This book is essentially a compilation of several research results contributed by the authors and their collaborators to the area of statistical analysis of ranking data. ... This book is suitable for researchers and analysts in various domains like web commerce, health analytics, and so on, where invariably there is lot of data for analysis and inference. The two facets presented in the book, nonparametric statistics and modeling, offer valuable tools for analysis and inference. (Laxminarayana Pillutla, Computing Reviews, May, 2015)


The book is written at the level of a research monograph and is best suited for senior undergraduate and graduate students. The procedures are often illustrated by applications to real data sets. ... the volume can very well serve as a textbook for courses on statistical methods for ranking data. (Lucia Santamaria, zbMATH 1341.62001, 2016) This book is essentially a compilation of several research results contributed by the authors and their collaborators to the area of statistical analysis of ranking data. ... This book is suitable for researchers and analysts in various domains like web commerce, health analytics, and so on, where invariably there is lot of data for analysis and inference. The two facets presented in the book, nonparametric statistics and modeling, offer valuable tools for analysis and inference. (Laxminarayana Pillutla, Computing Reviews, May, 2015)


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