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

Showing results 1 to 20 of 47.
Sorted by year (citations)

1 2 3 next

  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)

1 2 3 next