MLwiN

MLwiN is a statistical software package for fitting multilevel models. It uses both maximum likelihood estimation and Markov Chain Monte Carlo (MCMC) methods. MLwiN is based on an earlier package, MLn, but with a graphical user interface (as well as other additional features)[1]. MLwiN represents multilevel models using mathematical notation including Greek letters and multiple subscripts, so the user needs to be (or become) familiar with such notation.


References in zbMATH (referenced in 105 articles )

Showing results 41 to 60 of 105.
Sorted by year (citations)
  1. Yucel, Recai M.: Random covariances and mixed-effects models for imputing multivariate multilevel continuous data (2011)
  2. Candel, Math J. J. M.; Van Breukelen, Gerard J. P.: (D)-optimality of unequal versus equal cluster sizes for mixed effects linear regression analysis of randomized trials with clusters in one treatment arm (2010)
  3. Coertjens, Liesje; Pauw, Jelle Boeve-De; De Maeyer, Sven; Van Petegem, Peter: Do schools make a difference in their students’ environmental attitudes and awareness? Evidence from PISA 2006 (2010) MathEduc
  4. De Witte, K.; Thanassoulis, E.; Simpson, G.; Battisti, G.; Charlesworth-May, A.: Assessing pupil and school performance by non-parametric and parametric techniques (2010)
  5. Goetz, Thomas; Cronjaeger, Hanna; Frenzel, Anne C.; Lüdtke, Oliver; Hall, Nathan C.: Academic self-concept and emotion relations: domain specificity and age effects (2010) MathEduc
  6. Hox, Joop: Multilevel analysis. Techniques and applications (2010)
  7. Jarrod Hadfield: MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package (2010) not zbMATH
  8. Yucel, Recai M.; Demirtas, Hakan: Impact of non-normal random effects on inference by multiple imputation: a simulation assessment (2010)
  9. Feddag, M.-L.; Bacci, S.: Pairwise likelihood for the longitudinal mixed Rasch model (2009)
  10. Goldstein, Harvey; Carpenter, James; Kenward, Michael G.; Levin, Kate A.: Multilevel models with multivariate mixed response types (2009)
  11. Hossain, Md. Monir; Lawson, Andrew B.: Approximate methods in Bayesian point process spatial models (2009)
  12. Kenward, Michael G.; Carpenter, James R.: Comments on: Missing data methods in longitudinal studies: a review (2009)
  13. Candel, Math J. J. M.; Van Breukelen, Gerard J. P.; Kotova, Larissa; Berger, Martijn P. F.: Optimality of equal vs. unequal cluster sizes in multilevel intervention studies: A Monte Carlo study for small sample sizes (2008)
  14. Goldstein, Harvey; Kounali, Daphne; Robinson, Anthony: Modelling measurement errors and category misclassifications in multilevel models (2008)
  15. Nelson, Kerrie P.; Leroux, Brian G.: Properties and comparison of estimation methods in a log-linear generalized linear mixed model (2008)
  16. Oliveira, Mónica D.; Bevan, Gwyn: Modelling hospital costs to produce evidence for policies that promote equity and efficiency (2008)
  17. Rabe-Hesketh, Sophia; Skrondal, Anders: Multilevel and longitudinal modeling using Stata (2008)
  18. Rabe-Hesketh, S.; Skrondal, A.; Gjessing, H. K.: Biometrical modeling of twin and family data using standard mixed model software (2008)
  19. Shi, Lei; Chen, Gemai: Local influence in multilevel models (2008)
  20. Tzala, Evangelia; Best, Nicky: Bayesian latent variable modelling of multivariate spatio-temporal variation in cancer mortality (2008)