Results 11 to 20 of about 1,274,599 (258)
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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Bayesian Inference for Spatial Beta Generalized Linear Mixed Models [PDF]
Journal of Sciences, Islamic Republic of Iran, 2018 In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model
L. Kalhori Nadrabadi, M. Mohhamadzadeh
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Generalized fiducial inference for normal linear mixed models [PDF]
, 2012 While linear mixed modeling methods are foundational concepts introduced in any statistical education, adequate general methods for interval estimation involving models with more than a few variance components are lacking, especially in the unbalanced ...
Cisewski, Jessi, Hannig, Jan
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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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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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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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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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Pyglmnet : Python implementation of elastic-net regularized generalized linear models [PDF]
, 2020 Graceful handling of small Hessian term in coordinate descent solver that led to exploding update term Ensure full compatibility of GLM class with scikit ...
Achakulvisut, Titipat +21 more
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Sensitivity analysis for causal effects with generalized linear models
Journal of Causal Inference, 2022 Residual confounding is a common source of bias in observational studies. In this article, we build upon a series of sensitivity analyses methods for residual confounding developed by Brumback et al. and Chiba whose sensitivity parameters are constructed
Sjölander Arvid +2 more
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