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

2011 ◽  
Vol 55 (12) ◽  
pp. 3123-3134 ◽  
Author(s):  
Göran Broström ◽  
Henrik Holmberg
Keyword(s):  
Random Effects ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Clustered Data ◽  
Random Effects Models ◽  
Fixed And Random Effects

Random Effects Models for Personal Networks

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Jeroen K. Vermunt ◽  
Matthijs Kalmijn
Keyword(s):  
Marital Status ◽  
Random Effects ◽  
Generalized Linear Models ◽  
Latent Class ◽  
Linear Models ◽  
Personal Networks ◽  
Outcome Variable ◽  
Random Effects Models ◽  
Categorical Response ◽  
Age Category

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.

Generalized Linear Models with Random Effects

2006 ◽  
Author(s):  
Youngjo Lee ◽  
John A. Nelder ◽  
Yudi Pawitan
Keyword(s):  
Random Effects ◽  
Generalized Linear Models ◽  
Linear Models

A MIXTURE MODEL FOR PAYMENTS AND PAYMENT NUMBERS IN CLAIMS RESERVING

2016 ◽  
Vol 48 (1) ◽  
pp. 25-53 ◽  
Author(s):  
Patrizia Gigante ◽  
Liviana Picech ◽  
Luciano Sigalotti
Keyword(s):  
Random Effects ◽  
Linear Models ◽  
External Information ◽  
Parameter Estimates ◽  
Prediction Errors ◽  
Poisson Regression Model ◽  
External Data ◽  
Fixed And Random Effects ◽  
Likelihood Approach ◽  
The Impact

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.

A characterization of admissible linear estimators of fixed and random effects in linear models

Metrika ◽  
2007 ◽  
Vol 68 (2) ◽  
pp. 157-172 ◽  
Author(s):  
Ewa Synówka-Bejenka ◽  
Stefan Zontek
Keyword(s):  
Random Effects ◽  
Linear Models ◽  
Linear Estimators ◽  
Fixed And Random Effects
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