Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
  • First Online:
Statistical Methods for Ranking Data

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

  • 3266 Accesses

Abstract

This book was motivated by a desire to make available in a single volume many of the results on ranking methods developed by the authors and their collaborators that have appeared in the literature over a period of several years. In many instances, the presentations have a geometric flavor to them. As well there is a concerted effort to introduce real applications in order to exhibit the wide scope of ranking methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

  • Alvo, M., & Cabilio, P. (1992). Correlation methods for incomplete rankings. (Technical Report 200) Laboratory for Research in Statistics and Probability: Carleton University and University of Ottawa.

    Google Scholar 

  • Barnes, S. H., & Kaase, M. (1979). Political action: Mass participation in five western countries. London: Sage.

    Google Scholar 

  • Beggs, S., Cardell, S., & Hausman, J. (1981). Assessing the potential demand for electric cars. Journal of Econometrics, 16, 1–19.

    Article  Google Scholar 

  • Benter, W. (1994). Computer-based horse race handicapping and wagering systems: A report. In W. T. Ziemba, V. S. Lo, & D. B. Haush (Eds.), Efficiency of racetrack betting markets (pp. 183–198). San Diego: Academic.

    Google Scholar 

  • Bockenholt, U. (2001). Mixed-effects analysis of rank-ordered data. Psychometrika, 66(1), 45–62.

    Article  MathSciNet  Google Scholar 

  • Chapman, R., & Staelin, R. (1982). Exploiting rank ordered choice set data within the stochastic utility model. Journal of Marketing Research, 19, 288–301.

    Article  Google Scholar 

  • Craig, B. M., Busschbach, J. J. V., & Salomon, J. A. (2009). Modeling ranking, time trade-off, and visual analog scale values for eq-5d health states: A review and comparison of methods. Medical Care, 47(6), 634–641.

    Article  Google Scholar 

  • Croon, M. A. (1989). Latent class models for the analysis of rankings. In G. D. Soete, H. Feger, & K. C. Klauer (Eds.), New developments in psychological choice modeling (pp. 99–121). North-Holland: Elsevier Science.

    Chapter  Google Scholar 

  • Decarlo, L. T., & Luthar, S. S. (2000). Analysis and class validation of a measure of parental values perceived by early adolescents: An application of a latent class models for rankings. Educational and Psychological Measurement, 60(4), 578–591.

    Article  Google Scholar 

  • Diaconis, P. (1988). Group representations in probability and statistics. Hayward: Institute of Mathematical Statistics.

    MATH  Google Scholar 

  • Dittrich, R., Katzenbeisser, W., & Reisinger, H. (2000). The analysis of rank ordered preference data based on Bradley-Terry type models. OR Spektrum, 22, 117–134.

    Article  MATH  Google Scholar 

  • Duncan, O. D., & Brody, C. (1982). Analyzing n rankings of three items. In R. M. Hauser, D. Mechanic, A. O. Haller, & T. S. Hauser (Eds.), Social structure and behavior (pp. 269–310). New York: Academic..

    Chapter  Google Scholar 

  • Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric statistical inference (5th ed.). New York: Chapman Hall.

    MATH  Google Scholar 

  • Goldberg, A. I. (1975). The relevance of cosmopolitan local orientations to professional values and behavior. Sociology of Work and Occupation, 3, 331–356.

    Article  Google Scholar 

  • Gormley, I. C., & Murphy, T. B. (2008). Exploring voting blocs within the Irish electorate: A mixture modeling approach. Journal of the American Statistical Association, 103, 1014–1027.

    Article  MATH  MathSciNet  Google Scholar 

  • Hausman, J., & Ruud, P. A. (1987). Specifying and testing econometric models for rank-ordered data. Journal of Econometrics, 34, 83–104.

    Article  MATH  MathSciNet  Google Scholar 

  • Henery, R. J. (1981). Permutation probabilities as models for horse races. Journal of the Royal Statistical Society Series B, 43, 86–91.

    MathSciNet  Google Scholar 

  • Higgins, J. J. (2004). An introduction to modern nonparametric statistics. Pacific Grove: Brooks Cole-Thomson.

    Google Scholar 

  • Inglehart, R. (1977). The silent revolution: Changing values and political styles among western publics. Princeton: Princeton University Press.

    Google Scholar 

  • Kamishima, T., & Akaho, S. (2006). Efficient clustering for orders. In Proceedings of the 2nd International Workshop on Mining Complex Data, Hong Kong, China (pp. 274–278).

    Google Scholar 

  • Koop, G., & Poirier, D. J. (1994). Rank-ordered logit models: An empirical analysis of ontario voter preferences. Journal of Applied Econometrics, 9(4), 69–388.

    Article  Google Scholar 

  • Krabbe, P. F. M., Salomon, J. A., & Murray, C. J. L. (2007). Quantification of health states with rank-based nonmetric multidimensional scaling. Medical Decision Making, 27, 395–405.

    Article  Google Scholar 

  • Luce, R. D. (1959). Individual choice behavior. New York: Wiley.

    MATH  Google Scholar 

  • Maydeu-Olivares, A., & Bockenholt, U. (2005). Structural equation modeling of paired-comparison and ranking data. Psychological Methods, 10(3), 285–304.

    Article  Google Scholar 

  • McCabe, C., Brazier, J., Gilks, P., Tsuchiya, A., Roberts, J., O’Hagan, A., & Stevens, K. (2006). Use rank data to estimate health state utility models. Journal of Health Economics, 25, 418–431.

    Article  Google Scholar 

  • Moors, G., & Vermunt, J. (2007). Heterogeneity in post-materialists value priorities. Evidence from a latent class discrete choice approach. European Sociological Review, 23, 631–648.

    Google Scholar 

  • Murphy, T. B., & Martin, D. (2003). Mixtures of distance-based models for ranking data. Computational Statistics and Data Analysis, 41, 645–655.

    Article  MATH  MathSciNet  Google Scholar 

  • Nombekela, S. W., Murphy, M. R., Gonyou, H. W., & Marden, J. I. (1993). Dietary preferences in early lactation cows as affected by primary tastes and some common feed flavors. Journal of Diary Science, 77, 2393–2399.

    Article  Google Scholar 

  • Plumb, A. A. O., Grieve, F. M., & Khan, S. H. (2009). Survey of hospital clinicians’ preferences regarding the format of radiology reports. Clinical Radiology, 64, 386–394.

    Article  Google Scholar 

  • Ratcliffe, J., Brazaier, J., Tsuchiya, A., Symonds, T., & Brown, M. (2006). Estimation of a preference based single index from the sexual quality of life questionnaire (SQOL) using ordinal data. Discussion Paper Series, Health Economics and Decision Science, The University of Sheffield, 06, 6.

    Google Scholar 

  • Ratcliffe, J., Brazaier, J., Tsuchiya, A., Symonds, T., & Brown, M. (2009). Using DCE and ranking data to estimate cardinal values for health states for deriving a preference-based single index from the sexual quality of life questionnaire. Health Economics, 18, 1261–1276.

    Article  Google Scholar 

  • Regenwetter, M., Ho, M. H. R., & Tsetlin, I. (2007). Sophisticated approval voting, ignorance priors, and plurality heuristics: A behavioral social choice analysis in a Thurstonian framework. Psychological Review, 114(4), 994–1014.

    Article  Google Scholar 

  • Riketta, M., & Vonjahr, D. (1999). Multidimensional scaling of ranking data for different age groups. Experimental Psychology, 46(4), 305–311.

    Article  Google Scholar 

  • Salomon, J. A. (2003). Reconsidering the use of rankings in the valuation of health states: A model for estimating cardinal values from ordinal data. Population Health Metrics, 1, 1–12.

    Article  Google Scholar 

  • Skrondal, A., & Rabe-Hesketh, S. (2003). Multilevel logistic regression for polytomous data and rankings. Psychometrika, 68(2), 267–287.

    Article  MathSciNet  Google Scholar 

  • Stern, H. (1990b). Models for distributions on permutations. Journal of the American Statistical Association, 85, 558–564.

    Article  Google Scholar 

  • Stern, H. (1993). Probability models on rankings and the electoral process. In M. A. Fligner & J. S. Verducci (Eds.), Probability models and statistical analyses for ranking data (pp. 173–195). New York: Springer.

    Chapter  Google Scholar 

  • Vermunt, J. K. (2004). Multilevel latent class models. Sociological Methodology, 33, 213–239.

    Article  MathSciNet  Google Scholar 

  • Vigneau, E., Courcoux, P., & Semenou, M. (1999). Analysis of ranked preference data using latent class models. Food Quality and Preference, 10, 201–207.

    Article  Google Scholar 

  • Yu, P. L. H., & Chan, L. K. Y. (2001). Bayesian analysis of wandering vector models for displaying ranking data. Statistica Sinica, 11, 445–461.

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON, Canada

    Mayer Alvo

  2. Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China

    Philip L. H. Yu

Authors
  1. Mayer Alvo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philip L. H. Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Alvo, M., Yu, P.L.H. (2014). Introduction. In: Statistical Methods for Ranking Data. Frontiers in Probability and the Statistical Sciences. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1471-5_1

Download citation

Publish with us

Policies and ethics

Search

Navigation