References in zbMATH (referenced in 28 articles , 1 standard article )

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  1. Detmer, Felicitas J.; Cebral, Juan; Slawski, Martin: A note on coding and standardization of categorical variables in (sparse) group Lasso regression (2020)
  2. Berger, Moritz; Welchowski, Thomas; Schmitz-Valckenberg, Steffen; Schmid, Matthias: A classification tree approach for the modeling of competing risks in discrete time (2019)
  3. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  4. Grilli, Leonardo; Rampichini, Carla: Discussion of `The class of CUB models: statistical foundations, inferential issues and empirical evidence’ by Domenico Piccolo and Rosaria Simone (2019)
  5. Piccolo, Domenico; Simone, Rosaria: The class of \textsccubmodels: statistical foundations, inferential issues and empirical evidence (2019)
  6. D’Ambra, Luigi; Amenta, Pietro; D’Ambra, Antonello: Decomposition of cumulative chi-squared statistics, with some new tools for their interpretation (2018)
  7. Hothorn, Torsten: Book review of: G. Tutz, Regression for categorical data (2017)
  8. Jeon, Jong-June; Kwon, Sunghoon; Choi, Hosik: Homogeneity detection for the high-dimensional generalized linear model (2017)
  9. Tutz, Gerhard; Schneider, Micha; Iannario, Maria; Piccolo, Domenico: Mixture models for ordinal responses to account for uncertainty of choice (2017)
  10. De Bin, Riccardo; Janitza, Silke; Sauerbrei, Willi; Boulesteix, Anne-Laure: Subsampling versus bootstrapping in resampling-based model selection for multivariable regression (2016)
  11. Gottard, Anna; Iannario, Maria; Piccolo, Domenico: Varying uncertainty in CUB models (2016)
  12. Iannario, Maria: Testing overdispersion in CUBE models (2016)
  13. Iannario, Maria; Piccolo, Domenico: A comprehensive framework of regression models for ordinal data (2016)
  14. Iannario, Maria; Piccolo, Domenico: A generalized framework for modelling ordinal data (2016)
  15. Janitza, Silke; Tutz, Gerhard; Boulesteix, Anne-Laure: Random forest for ordinal responses: prediction and variable selection (2016)
  16. Manisera, Marica; Zuccolotto, Paola: Treatment of `don’t know’ responses in a mixture model for rating data (2016)
  17. Rospleszcz, Susanne; Janitza, Silke; Boulesteix, Anne-Laure: Categorical variables with many categories are preferentially selected in bootstrap-based model selection procedures for multivariable regression models (2016)
  18. Schauberger, Gunther; Tutz, Gerhard: Detection of differential item functioning in Rasch models by boosting techniques (2016)
  19. Bürgin, Reto; Ritschard, Gilbert: Tree-based varying coefficient regression for longitudinal ordinal responses (2015)
  20. Manisera, Marica; Zuccolotto, Paola: Identifiability of a model for discrete frequency distributions with a multidimensional parameter space (2015)

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