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Generalized Linear Models [PDF]
Journal of the Royal Statistical Society. Series A (General), 1972 The technique of iterative weighted linear regression can be used to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation.
Nelder, J. A., Wedderburn, R. W. M.
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Hierarchical Generalized Linear Models: The R Package HGLMMM [PDF]
Journal of Statistical Software, 2011 The R package HGLMMM has been developed to fit generalized linear models with random effects using the h-likelihood approach. The response variable is allowed to follow a binomial, Poisson, Gaussian or gamma distribution.
Marek Molas, Emmanuel Lesaffre
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Regularization Paths for Generalized Linear Models via Coordinate Descent [PDF]
Journal of Statistical Software, 2010 We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge
Jerome Friedman +2 more
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Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation [PDF]
, 2011 Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates.
Groll, Andreas
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Finite Mixtures of Generalized Linear Regression Models [PDF]
, 2007 Generalized linear models have become a standard technique in the statistical modelling toolbox for investigating relationships between variables.
Bettina Grün +3 more
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Generalized Linear Models in Vehicle Insurance
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2014 Actuaries in insurance companies try to find the best model for an estimation of insurance premium. It depends on many risk factors, e.g. the car characteristics and the profile of the driver.
Silvie Kafková, Lenka Křivánková
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ParMA: Parallelized Bayesian Model Averaging for Generalized Linear Models
Journal of Statistical Software, 2022 This paper describes the gretl function package ParMA, which provides Bayesian model averaging (BMA) in generalized linear models. In order to overcome the lack of analytical specification for many of the models covered, the package features an ...
Riccardo (Jack) Lucchetti, Luca Pedini
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Quantifying Reserve Uncertainty Using Stochastic Multivariate Generalized Linear Model: A Case Study on Egyptian General Insurance Market [PDF]
Maǧallaẗ Al-Buḥūṯ Al-Mālīyyaẗ wa Al-TiğāriyyaẗAccurate claims reserving is crucial for insurance companies as it directly influences risk assessment, pricing strategies, and overall financial position. Traditional univariate reserving approaches, which treat each line of business independently, fail
شانا يوسف عبدالله +2 more
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Analysis of Robust Quasi-deviances for Generalized Linear Models
Journal of Statistical Software, 2004 Generalized linear models (McCullagh and Nelder 1989) are a popular technique for modeling a large variety of continuous and discrete data. They assume that the response variables Yi , for i = 1, . . .
Eva Cantoni
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Boosting Correlation Based Penalization in Generalized Linear Models [PDF]
, 2007 In high dimensional regression problems penalization techniques are a useful tool for estimation and variable selection. We propose a novel penalization technique that aims at the grouping effect which encourages strongly correlated predictors to be in ...
Tutz, Gerhard, Ulbricht, Jan
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