Statistical Methods for Ranking Data

By: Mayer Alvo, Philip L.H. Yu

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Paperback | 17 September 2016

At a Glance

Paperback
288 Pages

Dimensions(cm)
23.5 x 15.49 x 1.52

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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.

Industry Reviews

"This book is a research monograph that provides a comprehensive mathematical treatment of some useful statistical methods for ranking data and exhibit the real applications of those statistical methods. ... I have no doubt that academics and practitioners who are interested in ranking data will appreciate this book for the detailed considerations given to the interplay between theory and applications of statistical methods for ranking data." (Hon Keung Tony Ng, Technometrics, Vol. 59 (3), July, 2017)

"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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