GLLAMM

The program gllamm runs in the statistical package Stata and estimates GLLAMMs (Generalized Linear Latent And Mixed Models) by maximum likelihood (see help gllamm after installation). gllamm maximises the marginal log-likelihood using Stata’s version of the Newton Raphson Algorithm (ml with method d0). In the case of discrete random effects or factors, the marginal log-likelihood is evaluated exactly whereas numerical integration is used for continuous (multivariate) normal random effects or factors. Two methods are available for numerical integration: Quadrature or adaptive quadrature. In both cases it is essential to make sure that a sufficient number of quadrature points has been used by comparing solutions with different numbers quadrature points. In most cases adaptive quadrature will perform better than ordinary quadrature. This is particularly the case if the cluster sizes are large and the responses include (large) counts and/or continuous variables. Even where ordinary quadrature performs well, adaptive quadrature often requires fewer quadrature points making it faster.


References in zbMATH (referenced in 61 articles , 1 standard article )

Showing results 1 to 20 of 61.
Sorted by year (citations)

1 2 3 4 next

  1. Banerjee, Trambak; Mukherjee, Gourab; Dutta, Shantanu; Ghosh, Pulak: A large-scale constrained joint modeling approach for predicting user activity, engagement, and churn with application to freemium mobile games (2020)
  2. Rockwood, Nicholas J.: Maximum likelihood estimation of multilevel structural equation models with random slopes for latent covariates (2020)
  3. Guerrier, Stéphane; Dupuis-Lozeron, Elise; Ma, Yanyuan; Victoria-Feser, Maria-Pia: Simulation-based bias correction methods for complex models (2019)
  4. Ippel, L.; Kaptein, M. C.; Vermunt, J. K.: Online estimation of individual-level effects using streaming shrinkage factors (2019)
  5. Kabaila, Paul; Ranathunga, Nishika: On adaptive Gauss-Hermite quadrature for estimation in GLMM’s (2019)
  6. Wang, Chun; Xu, Gongjun; Zhang, Xue: Correction for item response theory latent trait measurement error in linear mixed effects models (2019)
  7. Lu, Jing; Zhang, Jiwei; Tao, Jian: Slice-Gibbs sampling algorithm for estimating the parameters of a multilevel item response model (2018)
  8. Francesco Bartolucci and Claudia Pigini: cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models (2017) not zbMATH
  9. Niku, Jenni; Warton, David I.; Hui, Francis K. C.; Taskinen, Sara: Generalized linear latent variable models for multivariate count and biomass data in ecology (2017)
  10. Lee, Yongwoong; Rösch, Daniel; Scheule, Harald: Accuracy of mortgage portfolio risk forecasts during financial crises (2016)
  11. Marino, Maria Francesca; Alfó, Marco: Gaussian quadrature approximations in mixed hidden Markov models for longitudinal data: a simulation study (2016)
  12. Maruotti, Antonello; Raponi, Valentina; Lagona, Francesco: Handling endogeneity and nonnegativity in correlated random effects models: evidence from ambulatory expenditure (2016)
  13. Sheldrake, Richard: Differential predictors of under-confidence and over-confidence for mathematics and science students in England (2016) MathEduc
  14. Xia, Ye-Mao; Tang, Nian-Sheng; Gou, Jian-Wei: Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models (2016)
  15. Zaloumis, Sophie G.; Scurrah, Katrina J.; Harrap, Stephen B.; Ellis, Justine A.; Gurrin, Lyle C.: Non-proportional odds multivariate logistic regression of ordinal family data (2015)
  16. Adam Loy; Heike Hofmann: HLMdiag: A Suite of Diagnostics for Hierarchical Linear Models in R (2014) not zbMATH
  17. Lucas, Jean-Paul; Sébille, Véronique; Le Tertre, Alain; Le Strat, Yann; Bellanger, Lise: Multilevel modelling of survey data: impact of the two-level weights used in the pseudolikelihood (2014)
  18. Aksoy, Ozan; Weesie, Jeroen: Social motives and expectations in one-shot asymmetric prisoner’s dilemmas (2013)
  19. Cagnone, Silvia; Monari, Paola: Latent variable models for ordinal data by using the adaptive quadrature approximation (2013)
  20. Feddag, M.-L.: Composite likelihood estimation for multivariate probit latent traits models (2013)

1 2 3 4 next


Further publications can be found at: http://www.gllamm.org/pub.html