catdata

R package catdata: Categorical Data. This R-package contains examples from the book ”Regression for Categorical Data”, Tutz 2011, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.

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References in zbMATH (referenced in 47 articles , 1 standard article )

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  1. Zhang, Huiming; Jia, Jinzhu: Elastic-net regularized high-dimensional negative binomial regression: consistency and weak signal detection (2022)
  2. Ahn, Gilseung; Park, You-Jin; Hur, Sun: A membership probability-based undersampling algorithm for imbalanced data (2021)
  3. Berger, Moritz; Tutz, Gerhard: Transition models for count data: a flexible alternative to fixed distribution models (2021)
  4. Boente, Graciela; Salibián-Barrera, Matías: Robust functional principal components for sparse longitudinal data (2021)
  5. Liu, Dungang; Li, Shaobo; Yu, Yan; Moustaki, Irini: Assessing partial association between ordinal variables: quantification, visualization, and hypothesis testing (2021)
  6. Peyhardi, Jean; Fernique, Pierre; Durand, Jean-Baptiste: Splitting models for multivariate count data (2021)
  7. Scalera, Valentino; Iannario, Maria; Monti, Anna Clara: Robust link functions (2021)
  8. Sharifian, Nastaran; Bahrami Samani, Ehsan: A joint model for mixed longitudinal (k)-category inflation ordinal and continuous responses (2021)
  9. Simone, Rosaria: An accelerated EM algorithm for mixture models with uncertainty for rating data (2021)
  10. Wei, Zheng; Kim, Daeyoung: On exploratory analytic method for multi-way contingency tables with an ordinal response variable and categorical explanatory variables (2021)
  11. Yang, Xiaowei; Song, Shuang; Zhang, Huiming: Law of iterated logarithm and model selection consistency for generalized linear models with independent and dependent responses (2021)
  12. Capecchi, Stefania; Curtarelli, Maurizio: A mixture model to assess perception of discrimination on grounds of sexual orientation for policy considerations (2020)
  13. Detmer, Felicitas J.; Cebral, Juan; Slawski, Martin: A note on coding and standardization of categorical variables in (sparse) group Lasso regression (2020)
  14. Tutz, Gerhard: Modelling heterogeneity: on the problem of group comparisons with logistic regression and the potential of the heterogeneous choice model (2020)
  15. Tutz, Gerhard: On the structure of ordered latent trait models (2020)
  16. Berger, Moritz; Welchowski, Thomas; Schmitz-Valckenberg, Steffen; Schmid, Matthias: A classification tree approach for the modeling of competing risks in discrete time (2019)
  17. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  18. Choi, Hosik; Poythress, J. C.; Park, Cheolwoo; Jeon, Jong-June; Park, Changyi: Regularized boxplot via convex clustering (2019)
  19. Grilli, Leonardo; Rampichini, Carla: Discussion of “The class of CUB models: statistical foundations, inferential issues and empirical evidence” (2019)
  20. Piccolo, Domenico; Simone, Rosaria: The class of \textsccubmodels: statistical foundations, inferential issues and empirical evidence (2019)

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