Statistical Methods for Ranking Data
Alvo, Mayer; Yu, Philip L. H.
Sold by Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
AbeBooks Seller since February 27, 2001
New - Soft cover
Condition: New
Quantity: 15 available
Add to basketSold by Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
AbeBooks Seller since February 27, 2001
Condition: New
Quantity: 15 available
Add to basketSeries: Frontiers in Probability and the Statistical Sciences. Num Pages: 284 pages, 18 black & white illustrations, 4 colour illustrations, 60 black & white tables, biograph. BIC Classification: PBT; UFM; UNF. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 15. Weight in Grams: 444. . 2016. Softcover reprint of the original 1st ed. 2014. paperback. . . . .
Seller Inventory # V9781493947812
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.
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.
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