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＜E-Book＞
Statistical Methods for Ranking Data / by Mayer Alvo, Philip L.H. Yu
(Frontiers in Probability and the Statistical Sciences. ISSN:26249995)

Edition	1st ed. 2014.
Publisher	(New York, NY : Springer New York : Imprint: Springer)
Year	2014
Language	English
Size	XI, 273 p. 22 illus., 4 illus. in color : online resource
Authors	*Alvo, Mayer author Yu, Philip L.H author SpringerLink (Online service)
Subjects	LCSH:Statistics  LCSH:Mathematical statistics -- Data processing  All Subject Search LCSH:Data mining FREE:Statistical Theory and Methods FREE:Statistics FREE:Statistics and Computing FREE:Data Mining and Knowledge Discovery
Notes	Introduction -- Exploratory Analysis of Ranking Data -- Correlation Analysis of Paired Ranking Data -- Testing for randomness, agreement and interaction -- Block Designs -- General Theory of Hypothesis Testing -- Testing for Ordered Alternatives -- Probability Models for Ranking Data -- Probit Models for Ranking Data -- Decision Tree Models for Ranking Data -- Extension of Distance-Based Models for Ranking Data -- Appendix A: Ranking Data Sets -- Appendix B: Limit Theorems -- Appendix C: Review on Decision Trees 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 HTTP:URL=https://doi.org/10.1007/978-1-4939-1471-5

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