Generalized linear models with clustered data: Fixed and random effects models

2011 ◽  
Vol 55 (12) ◽  
pp. 3123-3134 ◽  
Author(s):  
Göran Broström ◽  
Henrik Holmberg
Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Jeroen K. Vermunt ◽  
Matthijs Kalmijn

We propose analyzing personal or ego-centered network data by means of two-level generalized linear models. The approach is illustrated with an example in which we assess whether personal networks are homogenous with respect to marital status after controlling for age homogeneity. In this example, the outcome variable is a bivariate categorical response variable (alter’s marital status and age category). We apply both factor-analytic parametric and latent-class-based nonparametric random effects models and compare the results obtained with the two approaches. The proposed models can be estimated with the Latent GOLD program for latent class analysis.


Author(s):  
Youngjo Lee ◽  
John A. Nelder ◽  
Yudi Pawitan

2016 ◽  
Vol 48 (1) ◽  
pp. 25-53 ◽  
Author(s):  
Patrizia Gigante ◽  
Liviana Picech ◽  
Luciano Sigalotti

AbstractWe consider a Tweedie's compound Poisson regression model with fixed and random effects, to describe the payment numbers and the incremental payments, jointly, in claims reserving. The parameter estimates are obtained within the framework of hierarchical generalized linear models, by applying the h-likelihood approach. Regression structures are allowed for the means and also for the dispersions. Predictions and prediction errors of the claims reserves are evaluated. Through the parameters of the distributions of the random effects, some external information (e.g. a development pattern of industry wide-data) can be incorporated into the model. A numerical example shows the impact of external data on the reserve and prediction error evaluations.


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