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Statistical Methods for Ranking Data

  • Mayer Alvo
  • Philip L.H. Yu

Part of the Frontiers in Probability and the Statistical Sciences book series (FROPROSTAS)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Mayer Alvo, Philip L. H. Yu
    Pages 1-5
  3. Mayer Alvo, Philip L. H. Yu
    Pages 7-21
  4. Mayer Alvo, Philip L. H. Yu
    Pages 23-53
  5. Mayer Alvo, Philip L. H. Yu
    Pages 55-79
  6. Mayer Alvo, Philip L. H. Yu
    Pages 81-104
  7. Mayer Alvo, Philip L. H. Yu
    Pages 105-125
  8. Mayer Alvo, Philip L. H. Yu
    Pages 127-147
  9. Mayer Alvo, Philip L. H. Yu
    Pages 149-169
  10. Mayer Alvo, Philip L. H. Yu
    Pages 171-198
  11. Mayer Alvo, Philip L. H. Yu
    Pages 199-222
  12. Mayer Alvo, Philip L. H. Yu
    Pages 223-238
  13. Back Matter
    Pages 239-273

About this book

Introduction

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.

Keywords

Block designs Exploratory data analysis Missing and tied data Probabilistic and statistical modeling Ranking data

Authors and affiliations

  • Mayer Alvo
    • 1
  • Philip L.H. Yu
    • 2
  1. 1.Department of Mathematics and StatisticsUniversity of OttawaOttawaCanada
  2. 2.Department of Statistics and Actuarial ScienceThe University of Hong KongHong KongChina

Bibliographic information