[1] Agresti, A. (1983). A simple diagonals-parameter symmetry and quasisymmetry model. Statistics and Probability Letters, 1, 313–316.
[2] Agresti, A. (1983). A survey of strategies for modeling crossclassifications having ordinal variables. Journal of the American Statistical Association, 78, 184–198.
[3] Agresti, A. (2002). Categorical Data Analysis, 2nd edition. Wiley, New York.
[4] Aitchison, J. (1962). Large-sample restricted parametric tests. Journal of the Royal Statistical Society, Ser. B, 24, 234–250.
[5] Bhapkar, V. P. and Darroch, J. N. (1990). Marginal symmetry and quasi symmetry of general order. Journal of Multivariate Analysis, 34, 173– 184.
[6] Bishop, Y. M. M., Fienberg, S. E., and Holland, P. W. (1975). Discrete Multivariate Analysis: Theory and Practice. The MIT Press, Cambridge, Massachusetts.
[7] Bowker, A. H. (1948). A test for symmetry in contingency tables. Journal of the American Statistical Association, 43, 572–574.
[8] Caussinus, H. (1965). Contribution `a l’analyse statistique des tableaux de corr´elation. Annales de la Facult´e des Sciences de l’Universit´e de Toulouse, 29, 77–182.
[9] Csisz´ar, I. and Shields, P. C. (2004). Information Theory and Statistics: A tutorial. Now Publishers, Hanover.
[10] Darroch, J. N. and Ratcliff, D. (1972). Generalized iterative scaling for log-linear models. The Annals of Mathematical Statistics 43, 1470–1490
[11] Darroch, J. N. and Silvey, S. D. (1963). On testing more than one hypothesis. The Annals of Mathematical Statistics, 34, 555–567.
[12] Fujisawa, K. and Tahata, K. (2018). Asymmetry models based on logit transformations for square contingency tables with ordinal categories. Journal of the Japanese Society of Computational Statistics, 30, 55–64.
[13] Fujisawa, K. and Tahata, K. (2020). Asymmetry model based on fdivergence and orthogonal decomposition of symmetry for square contingency tables with ordinal categories. SUT Journal of Mathematics, 56, 39–53.
[14] Gilula, Z. (1984). On some similarities between canonical correlation models and latent class models for two-way contingency tables. Biometrika, 71, 523–530.
[15] Gilula, Z., Krieger, A. M., and Ritov, Y. (1988). Ordinal association in contingency tables: some interpretive aspects. Journal of the American Statistical Association, 83.540–545.
[16] Goodman, L. A. (1968). The analysis of cross-classified data: Independence, quasi-independence, and interactions in contingency tables with or without missing entries. Journal of the American Statistical Association, 63, 1091–1131.
[17] Goodman, L. A. (1979). Multiplicative models for square contingency tables with ordered categories. Biometrika, 66, 413–418.
[18] Goodman, L. A. (1979). Simple models for the analysis of association in cross-classifications having ordered categories. Journal of the American Statistical Association, 74, 537–552.
[19] Goodman, L. A. (1985). The analysis of cross-classified data having ordered and/or unordered categories: association models, correlation models, and asymmetry models for contingency tables with or without missing entries. Annals of Statistics, 13, 10–69.
[20] Goodman, L. A. (1986). Some useful extensions of the usual correspondence analysis approach and the usual log-linear models approach in the analysis of contingency tables. International Statistical Review, 54, 243–309.
[21] Goodman, L. A. and Kruskal, W. H. (1954). Measures of association for cross classifications. Journal of the American Statistical Association, 49, 732–764.
[22] Ireland, C. T., Ku, H. H., and Kullback, S. (1969). Symmetry and marginal homogeneity of an r × r contingency table. Journal of the American Statistical Association, 64, 1323-1341.
[23] Kateri, M. (2014). Contingency Table Analysis: Methods and Implementation Using R. Birkh¨auser/Springer, New York.
[24] Kateri, M. (2018). ϕ-divergence in contingency table analysis Entropy, 20, 324.
[25] Kateri, M. and Agresti, A. (2007). A class of ordinal quasi-symmetry models for square contingency tables. Statistics and Probability Letters, 77, 598–603.
[26] Kateri, M. and Papaioannou, T. (1994). f-divergence association models. International Journal of Mathematical and Statistical Sciences, 3, 179–203.
[27] Kateri, M. and Papaioannou, T. (1997). Asymmetry models for contingency tables. Journal of the American Statistical Association, 92, 1124–1131.
[28] Kendall, M. G. (1945). The treatment of ties in rank problems. Biometrika, 33, 239–251.
[29] Ku, H. H., Varner, R. N., and Kullback, S. (1971). On the analysis of multidimensional contingency tables. Journal of the American Statistical Association, 66, 55–64.
[30] Kullback, S. (1959). Information Theory and Statistics, Wiley, New York.
[31] Lang, J. B. (1996). On the partitioning of goodness-of-fit statistics for multivariate categorical response models. Journal of the American Statistical Association, 91, 1017–1023.
[32] Lang, J. B. and Agresti, A. (1994). Simultaneously modeling joint and marginal distributions of multivariate categorical responses. Journal of the American Statistical Association, 89, 625–632.
[33] McCullagh, P. (1978). A class of parametric models for the analysis of square contingency tables with ordered categories. Biometrika, 65, 413–418.
[34] Pearson, K. (1900). On a criterion that a given system of deviations from the probable in the case of a correlated in system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine Series (5), 50, 157–172.
[35] Read, C. B. (1977). Partitioning chi-square in contingency tables: A teaching approach. Communications in Statistics-Theory and Methods, 6, 553–562.
[36] Read, T. R. C. and Cressie, N. A. C. (1988). Goodness-of-Fit Statistics for Discrete Multivariate Data. Springer-Verlag, New York.
[37] Shinoda, S., Tahata, K., Iki, K., and Tomizawa, S. (2019). Extended marginal homogeneity models based on complementary log-log transform for multi-way contingency tables. SUT Journal of Mathematics, 55, 25-37.
[38] Somers, R. H. (1962). A new asymmetric measure of association for ordinal variables. American Sociological Review, 27, 799–811.
[39] Stuart, A. (1955). A test for homogeneity of the marginal distributions in a two-way classification. Biometrika, 42, 412–416.
[40] Tahata, K. (2019). Separation of symmetry for square tables with ordinal categorical data. Japanese Journal of Statistics and Data Science, (Available https://link.springer.com/content/pdf/10.1007%2Fs42081- 019-00066-8.pdf, [29 August, 2020]).
[41] Tahata, K., Katakura, S., and Tomizawa, S. (2007). Decompositions of marginal homogeneity model using cumulative logistic models for multiway contingency tables. Revstat: Statistical Journal, 5, 163-176.
[42] Tahata, K., Naganawa, M., and Tomizawa, S. (2016). Extended linear asymmetry model and separation of symmetry for square contingency tables. Journal of the Japan Statistical Society, 46, 189–202.
[43] Tahata, K. and Tomizawa, S. (2011). Generalized linear asymmetry model and decomposition of symmetry for multiway contingency tables. Journal of Biometrics and Biostatistics, 2, 1–6.
[44] Tahata, K., Yamamoto, H., and Tomizawa, S. (2008). Orthogonality of decompositions of symmetry into extended symmetry and marginal equimoment for multi-way tables with ordered categories. Austrian Journal of Statistics, 37, 185–194.
[45] Tomizawa, S. (1987). Decompositions for 2-ratios-parameter symmetry model in square contingency tables with ordered categories. Biometrical Journal, 29, 45–55.
[46] Tomizawa, S. and Tahata, K. (2007). The analysis of symmetry and asymmetry: Orthogonality of decomposition of symmetry into quasisymmetry and marginal symmetry for multi-way tables. Journal de la Soci´et´e Fran¸caise de Statistique, 148, 3–36.
[47] Yoshimoto, T., Tahata, K., Saigusa, Y., and Tomizawa, S. (2019). Quasi point-symmetry models based on f-divergence and decomposition of point-symmetry for multi-way contingency tables. SUT Journal of Mathematics, 55, 109–137.