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Contingency Table Analysis pp 261–269Cite as

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Abstract

This epilogue chapter refers briefly to alternative methods and approaches in the analysis of contingency tables (latent class models, graphical models, and smoothing), not covered in the book. Furthermore, a bibliography on small sample inference, Bayesian inference, and the analysis of high-dimensional sparse contingency tables is discussed.

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References

  • Agresti, A.: Generalized odds ratios for ordinal data. Biometrics 36, 59–67 (1980)

    MATH  MathSciNet  Google Scholar 

  • Agresti, A.: A survey of exact inference for contingency tables (with discussion). Stat. Sci. 7, 131–177 (1992)

    MATH  MathSciNet  Google Scholar 

  • Agresti, A.: Exact inference for categorical data: recent advances and continuing controversies. Stat. Med. 20, 2709–2722 (2001)

    Google Scholar 

  • Agresti, A.: Analysis of Ordinal Categorical Data, 2nd edn. Wiley, New York (2010)

    MATH  Google Scholar 

  • Agresti, A.: Categorical Data Analysis, 3rd edn. Wiley, Hoboken (2013)

    MATH  Google Scholar 

  • Agresti, A., Chuang, C.: Model-based Bayesian methods for estimating cell proportions in cross-classification tables having ordered categories. Comput. Stat. Data Anal. 7, 245–258 (1989)

    MATH  MathSciNet  Google Scholar 

  • Agresti, A., Hitchcock, D.B.: Bayesian inference for categorical data analysis. Stat. Methods Appl. 14, 297–330 (2005)

    MATH  MathSciNet  Google Scholar 

  • Agresti, A., Kateri, M.: Some remarks on latent variable models in categorical data analysis. Commun. Stat. Theory Meth. 43, 1–14 (2014)

    MathSciNet  Google Scholar 

  • Agresti, A., Mehta, C.R., Patel, N.R.: Exact inference for contingency tables with ordered categories. J. Am. Stat. Assoc. 85, 453–458 (1990)

    MATH  MathSciNet  Google Scholar 

  • Aitchison, J., Aitken, C.G.: Multivariate binary discrimination by the kernel method. Biometrika 63, 413–420 (1976)

    MATH  MathSciNet  Google Scholar 

  • Albert, J.H.: Bayesian testing and estimation of association in a two-way contingency table. J. Am. Stat. Assoc. 92, 685–693 (1997)

    MATH  Google Scholar 

  • Albert, J.H., Chib, S.: Bayesian analysis of binary and polychotomous response data. J. Am. Stat. Assoc. 88, 669–679 (1993)

    MATH  MathSciNet  Google Scholar 

  • Altham, P.M.E.: Exact Bayesian analysis of a 2 × 2 contingency table, and Fisher’s ‘exact’ significance test. J. Roy. Stat. Soc. Ser. B 31, 261–269 (1969)

    MathSciNet  Google Scholar 

  • Altham, P.M.E.: The measurement of association of rows and columns for an r × c contingency table. J. Roy. Stat. Soc. Ser. B 32, 63–73 (1970)

    MATH  Google Scholar 

  • Altham, P.M.E.: The analysis of matched proportions. Biometrika 58, 561–576 (1971)

    MATH  Google Scholar 

  • Amiri, S., von Rosen, D.: On the efficiency of bootstrap method into the analysis contingency table. Comput. Meth. Programs Biomed. 104, 182–187 (2011)

    Google Scholar 

  • Anderson, C.J., Vermunt, J.K.: Log-multiplicative association models as latent variable models for nominal and/or ordinal data. Socio. Meth. 30, 81–121 (2000)

    Google Scholar 

  • Aoki, S., Takemura, A.: Markov chain Monte Carlo exact tests for incomplete two-way contingency tables. J. Stat. Comput. Simul. 75, 787–812 (2005)

    MATH  MathSciNet  Google Scholar 

  • Bartholomew, D., Knott, M., Moustaki, I.: Latent Variable Models and Factor Analysis: A Unified Approach, 3rd edn. Wiley, Hoboken (2011)

    Google Scholar 

  • Becker, M.P., Clogg, C.C.: A note on approximating correlations from odds ratios. Socio. Meth. Res. 16, 407–424 (1988)

    Google Scholar 

  • Becker, M.P., Yang, I.: Latent class marginal models for cross-classifications of counts. Socio. Meth. 28, 293–325 (1998)

    Google Scholar 

  • Bock, H.H.: Loglinear models and entropy clustering methods for qualitative data. In: Gaul, W., Schader, M. (eds.) Classification as a Tool of Research, pp. 19–26. Elsevier Science Publishers B.V. (North-Holland), Amsterdam (1986)

    Google Scholar 

  • Bonett, D.G., Price, R.M.: Inferential methods for the tetrachoric correlation coefficient. J. Educ. Behav. Stat. 30, 213–225 (2005)

    Google Scholar 

  • Bonett, D.G., Price, R.M.: Statistical inference for generalized Yule coefficients in 2 × 2 contingency tables. Socio. Meth. Res. 35, 429–446 (2007)

    MathSciNet  Google Scholar 

  • Bühlmann, P., van de Geer, S.: Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer, New York (2011)

    Google Scholar 

  • Chuang, C.: Empirical Bayes methods for a two-way multiplicative-interaction model. Commun. Stat. Theory Meth. 11, 2977–2989 (1982)

    MATH  Google Scholar 

  • Clayton, D.G.: Some odds ratio statistics for the analysis of ordered categorical data. Biometrika 61, 525–531 (1974)

    MATH  MathSciNet  Google Scholar 

  • Clayton, D.G.: An odds ratio comparison for ordered categorical data with censored observations. Biometrika 63, 405–408 (1976)

    MATH  Google Scholar 

  • Congdon, P.: Bayesian Models for Categorical Data. Wiley, New York (2005)

    MATH  Google Scholar 

  • Consonni, G., Pistone, G.: Algebraic Bayesian analysis of contingency tables with possibly zero-probability cells. Statistica Sinica 17, 1355–1370 (2007)

    MATH  MathSciNet  Google Scholar 

  • Coull, B.A., Agresti, A.: Generalized log-linear models with random effects, with application to smoothing contingency tables. Stat. Model. 0, 1–21 (2003)

    Google Scholar 

  • Cox, D.R.: Analysis of Binary Data. Chapman & Hall, New York (1970a)

    MATH  Google Scholar 

  • Cox, D.R., Snell, E.J.: Analysis of Binary Data, 2nd edn. Chapman & Hall, New York (1989)

    MATH  Google Scholar 

  • Dahinden, C., Parmigiani, G., Emerick, M.C., Bühlmann, P.: Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries. BMC Bioinformatics 8, 476 (2007) (http://www.biomedcentral.com/1471-2105/8/476).

  • Dahinden, C., Kalisch, M., and Bühlmann, P.: Decomposition and model selection for large contingency tables. Biometrical J. 52, 233–252 (2010)

    MATH  Google Scholar 

  • Darroch, J.N., Speed, T.P.: Additive and multiplicative models and interactions. Ann. Stat. 11, 724–738 (1983)

    MATH  MathSciNet  Google Scholar 

  • de Falguerolles, A., Jmel, S., Whittaker, J.: Correspondence analysis and association models constrained by a conditional independence graph. Psychometrika 60, 161–180 (1995)

    MATH  MathSciNet  Google Scholar 

  • Dellaportas, P., Forster, J.: Markov chain Monte Carlo model determination for hierarchical and graphical log-linear models. Biometrika 86, 615–633 (1999)

    MATH  MathSciNet  Google Scholar 

  • Diaconis, P., Sturmfels, B.: Algebraic algorithms for sampling from conditional distributions. Ann. Stat. 26, 363–397 (1998)

    MATH  MathSciNet  Google Scholar 

  • Digby, P.G.N.: Approximating the tetrachoric correlation coefficient. Biometrics 39, 753–757 (1983)

    Google Scholar 

  • Dobra, A.: Markov bases for decomposable graphical models. Bernoulli 9, 1093–1108 (2003)

    MATH  MathSciNet  Google Scholar 

  • Dobra, A., Fienberg, S.E.: Bounds for cell entries in contingency tables given marginal totals and decomposable graphs. Proc. Natl. Acad. Sci. 97, 11885–11892 (2000)

    MATH  MathSciNet  Google Scholar 

  • Dobra, A., Fienberg, S.E.: Bounding entries in multi-way contingency tables given a set of marginal totals. In: Haitovsky, Y., Lerche, H.R., Ritov, Y. (eds.) Foundations of Statistical Inference. Proceedings of the Shoresh Conference 2000, pp. 3–16. Springer, Berlin (2003)

    Google Scholar 

  • Dobra, A., Fienberg, S.E., Rinaldo, A., Slavkovic, A., Zhou, Y.: Algebraic statistics and contingency table problems: log-linear models, likelihood estimation, and disclosure limitation. In: Putinar, M., Sullivant, S. (eds.) Emerging Applications of Algebraic Geometry, pp. 63–88. Springer, New York (2009)

    Google Scholar 

  • Edwards, A.W.F.: The measure of association in a 2 × 2 Table. J. Roy. Stat. Soc. Ser. A 126, 109–114 (1963)

    Google Scholar 

  • Edwardes, M.D.deB., Baltzan, M.: The generalization of the odds ratio, risk ratio and risk difference to r × k tables. Stat. Med. 19, 1901–1914 (2000)

    Google Scholar 

  • Evans, M., Gilula, Z., Guttman, I.: Computational issues in the Bayesian analysis of categorical data: log-linear and Goodman’s RC model. Statistica Sinica 3, 391–406 (1993)

    MATH  MathSciNet  Google Scholar 

  • Everitt, B.S., Landau, S., Leese, M., Stahl, D.: Cluster Analysis, 5th edn. Wiley, New York (2011)

    MATH  Google Scholar 

  • Fienberg, S.E.: When did Bayesian inference become ‘Bayesian’? Bayesian Anal. 1, 1–40 (2006)

    MathSciNet  Google Scholar 

  • Formann, A.K.: Linear logistic latent class analysis for polytomous data. J. Am. Stat. Assoc. 87, 476–486 (1992)

    Google Scholar 

  • Forster, J.J.: Bayesian inference for Poisson and multinomial log-linear models. Stat. Meth. 7, 210–224 (2010)

    MATH  MathSciNet  Google Scholar 

  • Forster, J.J., McDonald, J.W., Smith, P.W.F.: Monte Carlo exact conditional tests for log-linear and logistic models. J. Roy. Stat. Soc. Ser. B 58, 445–453 (1996)

    MATH  MathSciNet  Google Scholar 

  • Geenens, G., Simar, L.: Nonparametric tests for conditional independence in two-way contingency tables. J. Multivariate Anal. 101, 765–788 (2010)

    MATH  MathSciNet  Google Scholar 

  • Gilula, Z.: Singular value decomposition of probability matrices: Probabilistic aspects of latent dichotomous variables. Biometrika 66, 339–344 (1979)

    MATH  MathSciNet  Google Scholar 

  • Gilula, Z.: On some similarities between canonical correlation models and latent class models for two-way contingency tables. Biometrika 71, 523–529 (1984)

    MATH  MathSciNet  Google Scholar 

  • Good, I.J.: On the estimation of small frequencies in contingency tables. J. Roy. Stat. Soc. Ser. B 18, 113–124 (1956)

    MATH  MathSciNet  Google Scholar 

  • Good, I.J.: The Estimation of Probabilities: An Essay on Modern Bayesian Methods. MIT Press, Cambridge (1965)

    MATH  Google Scholar 

  • Goodman, L.A.: Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 61, 215–231 (1974)

    MATH  MathSciNet  Google Scholar 

  • Goodman, L.A.: New methods for analyzing the intrinsic character of qualitative variables using cross- classified data. Am. J. Socio. 93, 529–583 (1987)

    Google Scholar 

  • Goodman, L.A.: Latent class analysis: the empirical study of latent types, latent variables and latent structures. In: Hagenaars, J.A., McCutcheon, A.L. (eds.) Applied Latent Class Analysis, pp. 3–55. Cambridge University Press, Cambridge (2002b)

    Google Scholar 

  • Goodman, L.A., Kruskal, W.H.: Measures of Association for Cross Classifications. Springer, New York (1979)

    MATH  Google Scholar 

  • Gottard, A., Marchetti, G.M., Agresti, A.: Quasi-symmetric graphical log-linear models. Scand. J. Stat. 38, 447–465 (2011)

    MATH  MathSciNet  Google Scholar 

  • Green, P.: Reversible Jump markov chain monte carlo computation and Bayesian model determination. Biometrika 82, 711–732 (1995)

    MATH  MathSciNet  Google Scholar 

  • Greenland, S.: Simpson’s paradox from adding constants in contingency tables as an example of Bayesian noncollapsibility. Am. Stat. 64, 340–344 (2010)

    MathSciNet  Google Scholar 

  • Haberman, S.J.: Analysis of Qualitative Data, vols. 1 and 2. Academic, New York (1979)

    Google Scholar 

  • Hagenaars, J.A.: Categorical causal modeling: latent class analysis and directed log-linear models with latent variables. Socio. Meth. Res. 26, 436–486 (1998)

    Google Scholar 

  • Hamdan, M.A.: Comparison of two measures of association in two-way contingency tables. Can. J. Stat. 5, 235–240 (1977)

    MathSciNet  Google Scholar 

  • Hara, H., Sei, T., Takemura, A.: Hierarchical subspace models for contingency tables. J. Multivariate Anal. 103, 19–34 (2012)

    MATH  MathSciNet  Google Scholar 

  • Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2009)

    Google Scholar 

  • Heinen, T.: Latent Class and Discrete Latent Trait Models. Sage Publications, Thousand Oaks (1996)

    Google Scholar 

  • Hirji, K.F.: Exact Analysis of Discrete Data. Chapman & Hall/CRC, Boca Raton (2006)

    MATH  Google Scholar 

  • Howard, J.V.: The 2 × 2 table: a discussion from a Bayesian viewpoint. Stat. Sci. 13, 351–367 (1998)

    MATH  Google Scholar 

  • Iliopoulos, G., Kateri, M., Ntzoufras, I.: Bayesian estimation of unrestricted and order-restricted association models for a two-way contingency table. Comput. Stat. Data Anal. 51, 4643–4655 (2007)

    MATH  MathSciNet  Google Scholar 

  • Iliopoulos, G., Kateri, M., Ntzoufras, I.: Bayesian model comparison for the order restricted RC association model. Psychometrika 74, 561–587 (2009)

    MATH  MathSciNet  Google Scholar 

  • Jeong, H.C., Jhun, M., Kim, D.: Bootstrap tests for independence in two-way ordinal contingency tables. Comput. Stat. Data Anal. 48, 623–631 (2005)

    MATH  MathSciNet  Google Scholar 

  • Jhun, M., Jeong, H.C.: Applications of bootstrap methods for categorical data analysis. Comput. Stat. Data Anal. 35, 83–91 (2000)

    MATH  MathSciNet  Google Scholar 

  • Kass, R.E., Raftery, A.E.: Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995)

    MATH  Google Scholar 

  • Kateri, M., Agresti, A.: Bayesian inference about odds ratio structure in ordinal contingency tables. Environmetrics 24, 281–288 (2013)

    MathSciNet  Google Scholar 

  • Kateri, M., Nicolaou, A., Ntzoufras, I.: Bayesian Inference for the RC(M) association model. J. Comput. Graph. Stat. 14, 116–138 (2005)

    MathSciNet  Google Scholar 

  • Kendall, M.G.: A new measure of rank correlation. Biometrika 30, 81–93 (1938)

    MATH  MathSciNet  Google Scholar 

  • Kendall, M.G.: Rank Correlation Methods. Charles Griffin, London (1948)

    MATH  Google Scholar 

  • King, R., Brooks, S.P.: Prior induction in log-linear models for general contingency table analysis. Ann. Stat. 29, 715–747 (2001)

    MATH  MathSciNet  Google Scholar 

  • Knuiman, M.W., Speed, T.P.: Incorporating prior information into the analysis of contingency tables. Biometrics 44, 1061–1071 (1988)

    MATH  MathSciNet  Google Scholar 

  • Krampe, A., Kuhnt, S.: Bowker’s test for symmetry and modifications within the algebraic framework. Comput. Stat. Data Anal. 51, 4124–4142 (2007)

    MATH  MathSciNet  Google Scholar 

  • Krampe, A., Kateri, M., Kuhnt, S.: Asymmetry models for square contingency tables: exact tests via algebraic statistics. Stat. Comput. 21, 55–67 (2011)

    MATH  MathSciNet  Google Scholar 

  • Laird, N.M.: Empirical Bayes methods for two-way contingency tables. Biometrika 65, 581–590 (1978)

    MATH  MathSciNet  Google Scholar 

  • Lancaster, H.O., Hamdan, M.A.: Estimation of the correlation coefficient in contingency tables with possible nonmetrical characters. Psychometrika 29, 383–391 (1964)

    MATH  MathSciNet  Google Scholar 

  • Lazarsfeld, P.F.: The logical and mathematical foundation of latent structure analysis. In: Suchman, E.A., Lazarsfeld, P.F., Starr, S.A., Clausen, J.A. (eds.) Studies in Social Psychology in World War II. Vol 4: Measurement and Prediction, pp. 362–412. Princeton University Press, Princeton (1950)

    Google Scholar 

  • Leonard, T.: Bayesian estimation methods for two-way contingency tables. J. Roy. Stat. Soc. B 37, 23–37 (1975)

    MATH  MathSciNet  Google Scholar 

  • Lindley, D.V.: The Bayesian analysis of contingency tables. Ann. Math. Stat. 35, 1622–1643 (1964)

    MATH  MathSciNet  Google Scholar 

  • Lupparelli, M., Marchetti, G.M., Bergsma, W.P.: Parameterizations and fitting of bi-directed graph models to categorical data. Scand. J. Stat. 36, 559–576 (2009)

    MATH  MathSciNet  Google Scholar 

  • Madigan, D., Raftery, A.E.: Model selection and accounting for model uncertainty in graphical models using Occam’s window. J. Am. Stat. Assoc. 89, 1535–1546 (1994)

    MATH  Google Scholar 

  • Madigan, D., York, J.: Bayesian graphical models for discrete data. Int. Stat. Rev. 63, 215–232 (1995)

    MATH  Google Scholar 

  • Massam, H., Liu, J., Dobra, A.: A conjugate prior for discrete hierarchical log-linear models. Ann. Stat. 37, 3431–3467 (2009)

    MATH  MathSciNet  Google Scholar 

  • McDonald, J.W., Smith, P.W.F.: Exact conditional tests of quasi-independence for triangular contingency tables: estimating attained significance levels. Appl. Stat. 44, 143–151 (1995)

    MATH  Google Scholar 

  • Mehta, C.R., Patel, N.R.: A network algorithm for performing Fisher’s exact test in r × c contingency tables. J. Am. Stat. Assoc. 78, 427–434 (1983)

    MATH  MathSciNet  Google Scholar 

  • Mehta, C.R., Patel, N.R.: Exact logistic regression: theory and examples. Stat. Med. 14, 2143–2160 (1995)

    Google Scholar 

  • Ng, K.W., Tang, M.L., Tan, M., Tian, G.L.: Grouped Dirichlet distribution: a new tool for incomplete categorical data analysis. J. Multivariate Anal. 99, 490–509 (2008)

    MATH  MathSciNet  Google Scholar 

  • Ntzoufras, I., Forster, J.J., Dellaportas, P.: Stochastic search variable selection for log-linear models. J. Stat. Comput. Simul. 68, 23–37 (2000)

    MATH  MathSciNet  Google Scholar 

  • Pearson, K.: Mathematical contribution to the theory of evolution VII: On the correlation of characters not quantitatively measurable. Phil. Trans. Roy. Soc. 195A, 1–47 (1900b)

    Google Scholar 

  • Pearson, K.: Mathematical contributions to the theory of evolution XIII: On the theory of contingency and its relation to association and normal correlation. Draper’s Co. Research Memoirs, Biometric Series, vol. 1. Dulau und Co., Londaon (1904) (Reprinted in Karl Pearson’s Early Statistical Papers ed. by E.S. Pearson. Cambridge University Press, Cambridge, 1948)

    Google Scholar 

  • Pearson, K.: On the probable error of a coefficient of correlation found from a fourfold table. Biometrika 9, 22–27 (1913)

    Google Scholar 

  • Pearson, K., Heron, D.: On theories of association. Biometrika 14, 186–191 (1913)

    Google Scholar 

  • Pettersson, T.: A comparative study of model-based tests of independence for ordinal data using the bootstrap. J. Stat. Comput. Simul. 72, 187–203 (2002)

    MATH  MathSciNet  Google Scholar 

  • Raftery, A.E.: A note on Bayes factors for log-linear contingency table models with vague prior information. J. Roy. Stat. Soc. B 48, 249–250 (1986)

    MathSciNet  Google Scholar 

  • Rapallo, F.: Algebraic Markov bases and MCMC for two-way contingency tables. Scand. J. Stat. 30, 385–397 (2003)

    MATH  MathSciNet  Google Scholar 

  • Rapallo, F.: Algebraic exact inference for rater agreement models. Stat. Meth. Appl. 14, 45–66 (2005)

    MATH  MathSciNet  Google Scholar 

  • Rapallo, F.: Markov bases and structural zeros. J. Symbolic Comput. 41, 164–172 (2006)

    MATH  MathSciNet  Google Scholar 

  • Rudas, T.: Odds Ratios in the Analysis of Contingency Tables. Series: Quantitative Applications in the Social Sciences. Sage Publications, Thousand Oaks (1998)

    Google Scholar 

  • Sauermann, W.: Bootstrapping the maximum likelihood estimator in high-dimensional log-linear models. Ann. Stat. 17, 1198–1216 (1989)

    MATH  MathSciNet  Google Scholar 

  • Scott, D.W., Tapia, R.A., Thompson, J.R.: Nonparametric probability density estimation by discrete maximum penalized-likelihood criteria. Ann. Stat. 8, 820–832 (1980)

    MATH  MathSciNet  Google Scholar 

  • Simon, G.A.: Efficacies of measures of association for ordinal contingency tables. J. Am. Stat. Assoc. 73, 545–551 (1978)

    MATH  Google Scholar 

  • Simonoff, J.S.: A penalty function approach to smoothing large sparse contingency tables. Ann. Stat. 11, 208–218 (1983)

    MATH  MathSciNet  Google Scholar 

  • Simonoff, J.S.: Smoothing categorical data. J. Stat. Plann. Infer. 47, 41–69 (1995)

    MATH  MathSciNet  Google Scholar 

  • Simonoff, J.S.: Three sides of smoothing: categorical data smoothing, nonparametric regression, and density estimation. Int. Stat. Rev. 66, 137–156 (1998)

    MATH  Google Scholar 

  • Somers, R.H.: A new asymmetric measure of association for ordinal variables. Am. Socio. Rev. 27, 799–811 (1962)

    MATH  Google Scholar 

  • Spiegelhalter, D.J., Smith, A.F.M.: Bayes factors for linear and log-linear models with vague prior information. J. Roy. Stat. Soc. Ser. B 44, 377–387 (1982)

    MATH  MathSciNet  Google Scholar 

  • Spiegelhalter, D.J., Best, N.G., Carlin, B.P., van der Linde, A.: Bayesian measures of model complexity and fit (with discussion). J. Roy. Stat. Soc. Ser. B 64, 583–639 (2002)

    MATH  Google Scholar 

  • Streitberg, B.: Exploring interactions in high-dimensional tables: a bootstrap alternative to log-linear models. Ann. Stat. 2, 405–413 (1999)

    MathSciNet  Google Scholar 

  • Stuart, A.: The estimation and comparison of strengths of association in contingency tables. Biometrika 40, 105–110 (1953)

    MATH  MathSciNet  Google Scholar 

  • Tarantola, C., Consonni, G., Dellaportas, P.: Bayesian clustering of row effects models. J. Stat. Plann. Infer. 138, 2223–2235 (2008)

    MATH  MathSciNet  Google Scholar 

  • Titterington, D.M., Bowman, A.W.: A comparative study of smoothing procedures for ordered categorical data. J. Stat. Comput. Simul. 21, 291–312 (1985)

    MathSciNet  Google Scholar 

  • Uebersax, J.S.: Statistical modeling of expert ratings on medical treatment appropriateness. J. Am. Stat. Assoc. 88, 421–427 (1993)

    Google Scholar 

  • Uebersax, J.S., Grove, W.M.: Latent class analysis of diagnostic agreement. Stat. Med. 9, 559–572 (1990)

    Google Scholar 

  • Uebersax, J.S., Grove, W.M.: A latent trait finite mixture model for the analysis of rating agreement. Biometrics 49, 823–835 (1993)

    MathSciNet  Google Scholar 

  • van de Geer, S.A.: High-dimensional generalized linear models and the lasso. Ann. Stat. 36, 614–645 (2008)

    MATH  Google Scholar 

  • van Mechelen, I., Bock, H.H., de Boeck, P.: Two-mode clustering methods: a structured overview. Stat. Meth. Med. Res. 13, 363–394 (2004)

    MATH  Google Scholar 

  • Warrens, M.J.: On association coefficients for 2 × 2 tables and properties that do not depend on the marginal distributions. Psychometrika 73, 777–789 (2008)

    MATH  MathSciNet  Google Scholar 

  • Webb, E.L., Forster, J.J.: Bayesian model determination for multivariate ordinal and binary data. Comput. Stat. Data Anal. 52, 2632–2649 (2008)

    MATH  MathSciNet  Google Scholar 

  • Yang, I., Becker, M.P: Latent variable modeling of diagnostic accuracy. Biometrics 53, 948–958 (1997)

    MATH  Google Scholar 

  • Yule, G.U.: On the association of attributes in statistics: with illustrations from the material of the Childhood Society & c. Phil. Trans. Ser. A 194, 257–319 (1900)

    MATH  Google Scholar 

  • Yule, G.U.: Notes on the theory of association of attributes in statistics. Biometrika 2, 121–134 (1903)

    Google Scholar 

  • Yule, G.U.: On the methods of measuring association between two attributes. J. Roy. Stat. Soc. 75, 579–642 (1912)

    Google Scholar 

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    Maria Kateri

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Kateri, M. (2014). Further Topics. In: Contingency Table Analysis. Statistics for Industry and Technology. Birkhäuser, New York, NY. https://doi.org/10.1007/978-0-8176-4811-4_10

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