Fahrmeir: Data from the book ”Multivariate Statistical Modelling Based on Generalized Linear Models”, first edition, by Ludwig Fahrmeir and Gerhard Tutz. Data and functions for the book ”Multivariate Statistical Modelling Based on Generalized Linear Models”, version 1, by Ludwig Fahrmeir and Gerhard Tutz

References in zbMATH (referenced in 248 articles )

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  1. Fokianos, Konstantinos; Fried, Roland; Kharin, Yuriy; Voloshko, Valeriy: Statistical analysis of multivariate discrete-valued time series (2022)
  2. Al-Dawsari, Sarah R.; Sultan, Khalaf S.: Modeling of daily confirmed Saudi COVID-19 cases using inverted exponential regression (2021)
  3. Berger, Moritz; Tutz, Gerhard: Transition models for count data: a flexible alternative to fixed distribution models (2021)
  4. Brown, Paul T.; Joshi, Chaitanya; Joe, Stephen; Rue, Håvard: A novel method of marginalisation using low discrepancy sequences for integrated nested Laplace approximations (2021)
  5. Chen, Yang; Luo, Ziyan; Kong, Lingchen: (\ell_2,0)-norm based selection and estimation for multivariate generalized linear models (2021)
  6. Lim, Alejandro; Chiang, Chin-Tsang; Teng, Jen-Chieh: Estimating robot strengths with application to selection of alliance members in FIRST robotics competitions (2021)
  7. Özkale, M. Revan; Nyquist, Hans: The stochastic restricted ridge estimator in generalized linear models (2021)
  8. Sharifian, Nastaran; Bahrami Samani, Ehsan: A joint model for mixed longitudinal (k)-category inflation ordinal and continuous responses (2021)
  9. Yang, Xiaowei; Song, Shuang; Zhang, Huiming: Law of iterated logarithm and model selection consistency for generalized linear models with independent and dependent responses (2021)
  10. Bry, Xavier; Trottier, Catherine; Mortier, Frédéric; Cornu, Guillaume: Component-based regularization of a multivariate GLM with a thematic partitioning of the explanatory variables (2020)
  11. Geirsson, Óli Páll; Hrafnkelsson, Birgir; Simpson, Daniel; Sigurdarson, Helgi: LGM split sampler: an efficient MCMC sampling scheme for latent Gaussian models (2020)
  12. Guo, Guangbao; Sun, Yue; Jiang, Xuejun: A partitioned quasi-likelihood for distributed statistical inference (2020)
  13. Wu, Ho-Hsiang; Ferreira, Marco A. R.; Elkhouly, Mohamed; Ji, Tieming: Hyper nonlocal priors for variable selection in generalized linear models (2020)
  14. Amini, Morteza; Roozbeh, Mahdi: Improving the prediction performance of the Lasso by subtracting the additive structural noises (2019)
  15. Fokianos, Konstantinos; Truquet, Lionel: On categorical time series models with covariates (2019)
  16. Lieli, Robert P.; Hsu, Yu-Chin: Using the area under an estimated ROC curve to test the adequacy of binary predictors (2019)
  17. Singh, Rakhi; Mukhopadhyay, Siuli: Exact Bayesian designs for count time series (2019)
  18. Xie, Yimeng; Xu, Li; Li, Jie; Deng, Xinwei; Hong, Yili; Kolivras, Korine; Gaines, David N.: Spatial variable selection and an application to Virginia Lyme disease emergence (2019)
  19. Bhattacharya, Arnab; Wilson, Simon P.: Sequential Bayesian inference for static parameters in dynamic state space models (2018)
  20. Bierkens, Joris; Bouchard-Côté, Alexandre; Doucet, Arnaud; Duncan, Andrew B.; Fearnhead, Paul; Lienart, Thibaut; Roberts, Gareth; Vollmer, Sebastian J.: Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains (2018)

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