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Tying up the loose ends in simple, multiple, joint correspondence analysis

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Compstat 2006 - Proceedings in Computational Statistics

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References

  1. Adachi, K. (2004) Oblique Promax rotation applied to the solutions in multiple correspondence analysis. Behaviormetrika 31: 1–12.

    Article  MATH  MathSciNet  Google Scholar 

  2. Aitchison, J. & Greenacre, M.J. (2002) Biplots of compositional data. Applied Statistics 51: 375–392.

    MATH  MathSciNet  Google Scholar 

  3. Benzécri, J.-P. (1973) L’Analyse des Données. Tôme I: l’Analyse des Correspondances. Dunod, Paris.

    MATH  Google Scholar 

  4. Benzécri, J.-P. (1979) Sur le calcul des taux d’inertie dans l’analyse d’un questionnaire. Cahiers de l’Analyse des Données 3: 55–71.

    Google Scholar 

  5. Gabriel, K.R. (1971). The biplot-graphical display with applications to principal component analysis. Biometrika 58: 453–467.

    Article  MATH  MathSciNet  Google Scholar 

  6. Gabriel, K.R. (2002). Goodness of fit of biplots and correspondence analysis. Biometrika 89, 423–436.

    Article  MATH  MathSciNet  Google Scholar 

  7. Gabriel, K.R. and Odoroff, C.L. (1990). Biplots in biomedical research. Statistics in Medicine 9: 423–436.

    Article  Google Scholar 

  8. Gifi, A. (1990). Nonlinear Multivariate Analysis. Wiley, Chichester, UK.

    MATH  Google Scholar 

  9. Gilula, Z. and Haberman, S. J. (1986). Canonical analysis of contingency tables by maximum likelihood. Journal of the American Statistical Association 81: 780–788.

    Article  MATH  MathSciNet  Google Scholar 

  10. Gower, J.C. and Hand, D.J. (1996). Biplots. Chapman and Hall, London.

    MATH  Google Scholar 

  11. Gower, J.C. (2006). Divided by a common language: analysing and visualizing two-way arrays. In M.J. Greenacre and J. Blasius (eds), Multiple Correspondence Analysis and Related Methods. Chapman and Hall, London, forthcoming.

    Google Scholar 

  12. Greenacre, M.J. (1984). Theory and Applications of Correspondence Analysis. Academic Press, London.

    MATH  Google Scholar 

  13. Greenacre, M.J. (1988). Correspondence analysis of multivariate categorical data by weighted least-squares. Biometrika 75: 457–467.

    Article  MATH  MathSciNet  Google Scholar 

  14. Greenacre, M.J. (1993a) Correspondence Analysis in Practice. Academic Press, London.

    Google Scholar 

  15. Greenacre, M.J. (1993b). Biplots in correspondence analysis. Journal of Applied Statistics 20: 251–269.

    Article  Google Scholar 

  16. Greenacre, M.J. (1993c). Multivariate generalizations of correspondence analysis. In C.M. Cuadras and C.R. Rao (eds), Multivariate Analysis: Future Directions 2, North Holland, Amsterdam, pp.327–340.

    Google Scholar 

  17. Greenacre, M.J. (1998). Diagnostics for joint displays in correspondence analysis. In J Blasius and M.J. Greenacre (eds), Visualization of Categorical Data. Academic Press, San Diego, pp. 221–238.

    Google Scholar 

  18. Greenacre, M.J. (2006a). Tying up the loose ends in simple correspondence analysis. Working Paper no. 940, Departament d’Economia i Empresa, Universitat Pompeu Fabra, Barcelona.

    Google Scholar 

  19. Greenacre, M.J. (2006b). From simple to multiple correspondence analysis. In M.J. Greenacre and J. Blasius (eds), Multiple Correspondence Analysis and Related Methods. Chapman and Hall, London, forthcoming.

    Google Scholar 

  20. Greenacre, M.J. and Blasius, J. (2006) (eds) Multiple Correspondence Analysis and Related Methods. Chapman and Hall, London, forthcoming.

    MATH  Google Scholar 

  21. Greenacre, M.J. and Lewi, P.J. (2005) Distributional equivalence and subcompositional coherence in the analysis of contingency tables, ratio-scale measurements and compositional data. Working Paper no. 908, Departament d’Economia i Empresa, Universitat Pompeu Fabra, Barcelona.

    Google Scholar 

  22. Greenacre, M.J. and Pardo, R. (2006). Subset correspondence analysis: visualizing relationships among a set of response categories from a questionnaire survey.. Sociological Methods and Research, forthcoming.

    Google Scholar 

  23. Hill, M.O. (1974) Correspondence analysis: a neglected multivariate method. Applied Statistics 23: 340–354.

    Article  Google Scholar 

  24. ISSP (1994). International Social Survey Program: Family and Changing Gender Roles II. Central Archive for Empirical Social Research, Cologne, Germany.

    Google Scholar 

  25. Lebart L. (1976). The significance of eigenvalues issued from correspondence analysis. In J. Gordesch and P. Naeve (eds), Proceedings in Computational Statistics, Physica Verlag, Vienna, pp. 38–45.

    Google Scholar 

  26. Lebart, L. (2006). Validation techniques in multiple correspondence analysis. In M.J. Greenacre and J. Blasius (eds), Multiple Correspondence Analysis and Related Methods. Chapman and Hall, London, forthcoming.

    Google Scholar 

  27. Legendre, P. (2001). Ecologically meaningful transformations for ordination of species data. Oecologia 129: 271–280

    Article  Google Scholar 

  28. Meulman, J. (1982). Homogeneity Analysis of Incomplete Data. DSWO Press, Leiden, The Netherlands.

    Google Scholar 

  29. Nenadiæ, O. and Greenacre, M.J. (2005) The computation of multiple correspondence analysis, with code in R. Working Paper no. 887, Departament d’Economia I Empresa, Universitat Pompeu Fabra, Barcelona.

    Google Scholar 

  30. Pagès, J. and Bécue-Bertaut, M. (2006). Multiple factor analysis for contingency tables. In M.J. Greenacre and J. Blasius (eds), Multiple Correspondence Analysis and Related Methods. Chapman and Hall, London, forthcoming.

    Google Scholar 

  31. R Development Core Team (2005). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org

    Google Scholar 

  32. Rao. C.R. (1995). A review of canonical coordinates and an alternative to correspondence analysis using Hellinger distance. Qüestiió 19: 23–63.

    Google Scholar 

  33. Silverman, B.W. and Titterington, D.M. (1980). Minimum covering ellipses. SIAM J. Sci. Stat. Comput. 1: 401–409.

    Article  MATH  MathSciNet  Google Scholar 

  34. Sokal, R. R. and Rohlf, F.J. (1981). Biometry: The Principles and Practice of Statistics in Biological Research. 2 nd Edition. W.H. Freeman & Co, New York.

    MATH  Google Scholar 

  35. Van de Velden, M. (2003). Some Topics in Correspondence Analysis. PhD Thesis, University of Amsterdam

    Google Scholar 

  36. Vermunt, J.K. and Anderson, C.J. (2005). Joint correspondence analysiebibliogs by maximum likelihood. Methodology 1: 18–26.

    Google Scholar 

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Greenacre, M. (2006). Tying up the loose ends in simple, multiple, joint correspondence analysis. In: Rizzi, A., Vichi, M. (eds) Compstat 2006 - Proceedings in Computational Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-1709-6_13

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