What’s Next?

  • Tamás Rudas
Part of the Springer Texts in Statistics book series (STS)


Readers who have followed through with studying the material presented in the book are now ready to read the literature leading to current research in the field. This brief chapter contains summaries of and references to interesting and useful topics.


  1. 2.
    Aitchison, J., Silvey, S., D.: Maximum-likelihood estimation procedures and associated tests of significance. Journal of the Royal Statistical Society, Ser. B, 22, 154–171 (1960)MathSciNetzbMATHGoogle Scholar
  2. 4.
    Andersson, S.A, Madigan, D., Perlman, M.: Alternative Markov properties for chain graphs. Scandinavian Journal of Statistics, 28, 33–85 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 8.
    Berg, B.A.: Markov Chain Monte Carlo Simulations and Their Statistical Analysis. World Scientific (2004)Google Scholar
  4. 9.
    Bergsma, W, Croon, M., Hagenaars, J.A.: Marginal Models For Dependent, Clustered and Longitudinal Categorical Data. Springer, New York (2009)zbMATHGoogle Scholar
  5. 10.
    Bergsma, W.P., Rudas, T.: Marginal models for categorical data. Annals of Statistics, 30, 140–159 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 13.
    Bishop, Y.M.M., Fienberg, S.E.,Holland, P.W.: Discrete Multivariate Analysis: Theory and Practice. MIT Press, Boston (1975)zbMATHGoogle Scholar
  7. 20.
    Clogg, C.C, Rudas, T., Matthews, S.: Analysis of model misfit, structure, and local structure in contingency tables using graphical displays based on the mixture index of fit. In Blajius, J., Greenacre, M. (ed.) Visualization of Categorical Data, pp. 425–439. Academic Press, New York (1997)Google Scholar
  8. 23.
    Diaconis, P., Efron, B.: Testing for independence in a two-way table: new interpretations of the chi-square statistic. Annals of Statistics, 13, 845–874 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 24.
    Drton, M.: Discrete chain graph models. Bernoulli 15, 736–753 (2009)Google Scholar
  10. 27.
    Formann, A.K.: Latent class model diagnostics – a review and some proposals. Computational Statistics and Data Analysis 41, 549–559 (2003)Google Scholar
  11. 31.
    Frydenberg, M.: The chain graph Markov property. Scandinavian Journal of Statistics 17, 333–353 (1990)Google Scholar
  12. 33.
    Goodman, L. A.: The analysis of multidimensional contingency tables, when some variables are posterior to others: a modified path analysis approach. Biometrika 60, 179–192 (1973)Google Scholar
  13. 40.
    Kawamura, G., Matsouka, T., Taijri, T., Nishida, M., Hayashi, M.: Effectiveness of a sugarcane-fish combination as bait in trapping swimming crabs. Fisheries Research, 22, 155–160 (1995)CrossRefGoogle Scholar
  14. 42.
    Klimova, A., Rudas, T.: Iterative scaling in curved exponential families. Scandinavian Journal of Statistics, 42, 832–847. (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 43.
    Klimova, A., Rudas, T.: Testing the fit of relational models. arxiv: 1612.02416v1 (2016)Google Scholar
  16. 44.
    Klimova, A., Rudas, T., Dobra, A.: Relational models for contingency tables. Journal of Multivariate Statistics, 104, 159–173. (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 45.
    Klimova, A., Uhler, C., Rudas, T.: Faithfulness and learning of hypergraphs from discrete distributions. Computational Statistics and Data Analysis, 87, 57–72. (2015)MathSciNetCrossRefGoogle Scholar
  18. 47.
    Koski, T, Noble, J.: Bayesian Networks: An Introduction. Wiley, New York (2009).CrossRefzbMATHGoogle Scholar
  19. 48.
    Lauritzen, S.L.: Graphical Models. Clarendon Press, Oxford (1996)zbMATHGoogle Scholar
  20. 49.
    Lauritzen, S.L:, Dawid, A.P., Larsen, B.N., Leimer, H.-G.: Independence properties of directed Markov fields. Networks, 20, 491–505 (1990)MathSciNetCrossRefGoogle Scholar
  21. 50.
    Lauritzen, S.L, Wemuth, N.: Graphical models for associations between variables, some of which are qualitative and some quantitative. Annals of Statistics, 17, 31–57 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 56.
    Lupparelli, M., Marchetti, G.M., Bergsma, W.: Parameterization and fitting of bi-directed graph models to categorical data. Scandinavian Journal of Statistics, 36, 559–576 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 60.
    Németh, R., Rudas, T: On the application of discrete marginal graphical models. Sociological Methofology, 43, 70–100 (2013)CrossRefGoogle Scholar
  24. 63.
    Pourret, O, Naim, P., Marcot, B.: Bayesian Networks: A Practical Guide to Applications. Wiley, New York (2008).CrossRefzbMATHGoogle Scholar
  25. 64.
    Read, T.R.C., Cressie, N.A.C.: Goodness-of-Fit Statistics for Discrete Multivariate Data. Springer, New York (1988)CrossRefzbMATHGoogle Scholar
  26. 66.
    Richardson, T.S., Spirtes, P.: Ancestral graph Markov models. Annals of Statistics, 30, 962–1030 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 67.
    Roberts, C.P., Casella, G.: Monte Carlo Statistical Methods, 2nd ed. Springer, New York (2013)Google Scholar
  28. 70.
    Rudas, T.: A Monte Carlo comparison of the small sample behaviour of the Pearson, the likelihood ratio and the Cressie-Read statistics. Journal of Statistical Computation and Simulation, 24, 107–120 (1986)CrossRefGoogle Scholar
  29. 73.
    Rudas, T.: The mixture ndex of fit and minimax regression. Metrika, 50, 163–172 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 75.
    Rudas, T.: Mixture models of missing data. Quality & Quantity, 39, 19–36 (2005)CrossRefGoogle Scholar
  31. 80.
    Rudas, T., Bergsma, W.: On applications of marginal models to categorical data. Metron, 42, 15–37 (2004)MathSciNetGoogle Scholar
  32. 81.
    Rudas, T., Bergsma, W., Németh, R.: Parameterization and estimation of path models for categorical data. in Rizzi, A., Vichi, M., eds. COMPSTAT 2006, 383–394, Physica Verlag, Heidelberg (2006)Google Scholar
  33. 82.
    Rudas, T., Bergsma, W., Németh, R.: Marginal log-linear parameterization of conditional independence models. Biometrika, 97, 1006–1012 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  34. 83.
    Rudas, T., Clogg, C.C., Lindsay, B.G.: A new index of fit based on mixture methods for the analysis of contingency tables. Journal of the Royal Statistical Society, Ser B, 56, 623–639 (1994)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Tamás Rudas
    • 1
    • 2
  1. 1.Center for Social SciencesHungarian Academy of SciencesBudapestHungary
  2. 2.Eötvös Loránd UniversityBudapestHungary

Personalised recommendations