Package glmmAK: Generalized Linear Mixed Models. This package implements maximum-likelihood estimation in the logistic regression with both binary (logit model) and multinomial response (cumulative logit model), and in the Poisson regression (log-linear model). Secondly, Bayesian estimation based on MCMC in the logistic and Poisson regression model with random effects whose distribution is specified as a penalized normal mixture are implemented. The methodology is described and the package used in: KOMÁREK, A. and LESAFFRE, E. (2008). Generalized linear mixed model with a penalized Gaussian mixture as a random-effects distribution. Computational Statistics and Data Analysis, 52(7), 3441–3458,

References in zbMATH (referenced in 21 articles )

Showing results 1 to 20 of 21.
Sorted by year (citations)

1 2 next

  1. Zhang, Tonglin: General Gaussian estimation (2019)
  2. Xu, Peirong; Peng, Heng; Huang, Tao: Unsupervised learning of mixture regression models for longitudinal data (2018)
  3. Cipolli, William III; Hanson, Timothy: Computationally tractable approximate and smoothed polya trees (2017)
  4. Zhang, Zhengxin; Hu, Changhua; Si, Xiaosheng; Zhang, Jianxun; Zheng, Jianfei: Stochastic degradation process modeling and remaining useful life estimation with flexible random-effects (2017)
  5. Bao, Junshu; Hanson, Timothy E.: A mean-constrained finite mixture of normals model (2016)
  6. Dalla Valle, Luciana; De Giuli, Maria Elena; Tarantola, Claudia; Manelli, Claudio: Default probability estimation via pair copula constructions (2016)
  7. Heinzl, Felix; Tutz, Gerhard: Additive mixed models with approximate Dirichlet process mixtures: the EM approach (2016)
  8. Oedekoven, C. S.; King, R.; Buckland, S. T.; Mackenzie, M. L.; Evans, K. O.; Burger, L. W.: Using hierarchical centering to facilitate a reversible jump MCMC algorithm for random effects models (2016)
  9. Mabon, G.: Adaptive estimation of marginal random-effects densities in linear mixed-effects models (2015)
  10. Dion, Charlotte: New adaptive strategies for nonparametric estimation in linear mixed models (2014)
  11. Jaspers, Stijn; Aerts, Marc; Verbeke, Geert; Beloeil, Pierre-Alexandre: A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance (2014)
  12. Kauermann, Göran; Meyer, Renate: Penalized marginal likelihood estimation of finite mixtures of Archimedean copulas (2014)
  13. Gałecki, Andrzej; Burzykowski, Tomasz: Linear mixed-effects models using R. A step-by-step approach (2013)
  14. Baghishani, Hossein; Rue, Håvard; Mohammadzadeh, Mohsen: On a hybrid data cloning method and its application in generalized linear mixed models (2012)
  15. Schellhase, Christian; Kauermann, Göran: Density estimation and comparison with a penalized mixture approach (2012)
  16. Broström, Göran; Holmberg, Henrik: Generalized linear models with clustered data: fixed and random effects models (2011)
  17. Hosseini, Fatemeh; Eidsvik, Jo; Mohammadzadeh, Mohsen: Approximate Bayesian inference in spatial GLMM with skew normal latent variables (2011)
  18. Sartori, N.; Severini, T. A.; Marras, E.: An alternative specification of generalized linear mixed models (2010)
  19. Claeskens, Gerda; Hart, Jeffrey D.: Rejoinder to: Goodness-of-fit tests in mixed models (2009)
  20. Claeskens, Gerda; Hart, Jeffrey D.: Goodness-of-fit tests in mixed models (2009)

1 2 next