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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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Comparative study of diagnostic methods for linear mixed models and generalized linear models. [PDF]
, 2022 graficas, tablasMuchos fenómenos de la naturaleza pueden ser representados por medio de modelos estadísticos de forma satisfactoria y, para validar estos modelos, los métodos de diagnóstico resultan ser herramientas muy útiles para la verificación de ...
Morales Foronda, Andrés Felipe
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Generalized linear mixed models can detect unimodal species-environment relationships [PDF]
PeerJ, 2013 Niche theory predicts that species occurrence and abundance show non-linear, unimodal relationships with respect to environmental gradients. Unimodal models, such as the Gaussian (logistic) model, are however more difficult to fit to data than linear ...
Tahira Jamil, Cajo J.F. ter Braak
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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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Designs for generalized linear models with random block effects via information matrix approximations [PDF]
, 2015 The selection of optimal designs for generalized linear mixed models is complicated by the fact that the Fisher information matrix, on which most optimality criteria depend, is computationally expensive to evaluate.
Waite, Timothy W +3 more
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Quasi-Monte Carlo EM algorithm for MLEs in generalized linear mixed models [PDF]
, 1998 Inferences for generalized linear mixed models are greatly hampered by the intractable integrated likelihood. In this paper numerical integration based on Quasi-Monte Carlo method is used to approximate the integral of the EM algorithm and then to fit ...
Robin Thompson +3 more
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Using R In Generalized Linear Models [PDF]
Revista Română de Statistică, 2015 This paper aims to approach the estimation of generalized linear models (GLM) on the basis of the glm routine package in R. Particularly, regression models will be analyzed for those cases in which the explained variable follows a Poisson or a Negative ...
Mihaela Covrig +4 more
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Mathematics, 2023 Generalized Linear Models (GLMs) are the standard tool used for pricing in the field of automobile insurance. Generalized Additive Models (GAMs) are more complex and computationally intensive but allow taking into account nonlinear effects without the ...
Zuleyka Díaz Martínez +2 more
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Formulating State Space Models in R with Focus on Longitudinal Regression Models [PDF]
Journal of Statistical Software, 2006 We provide a language for formulating a range of state space models with response densities within the exponential family. The described methodology is implemented in the R-package sspir. A state space model is specified similarly to a generalized linear
Claus Dethlefsen +1 more
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Introduction to general and generalized linear models [PDF]
Journal of Applied Statistics, 2012 Introduction to general and generalized linear models, by Henrik Madsen and Poul Thyregod, Boca Raton, Chapman & Hall/CRC Press, 2011, xii+302 pp., £ 39.99 or US$83.95 (hardback), ISBN 978-1-420-09...
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