Adeniyi, Isaac Adeola and Yahya, Waheed Babatunde (2020): Bayesian Generalized Linear Mixed Effects Models Using NormalIndependent Distributions: Formulation and Applications. Forthcoming in: AStAAdvances in Statistical Analysis

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Abstract
A standard assumption is that the random effects of Generalized Linear Mixed Effects Models (GLMMs) follow the normal distribution. However, this assumption has been found to be quite unrealistic and sometimes too restrictive as revealed in many reallife situations. A common case of departures from normality includes the presence of outliers leading to heavytailed distributed random effects. This work, therefore, aims to develop a robust GLMM framework by replacing the normality assumption on the random effects by the distributions belonging to the NormalIndependent (NI) class. The resulting models are called the NormalIndependent GLMM (NIGLMM). The four special cases of the NI class considered in these models’ formulations include the normal, Studentt, Slash and contaminated normal distributions. A full Bayesian technique was adopted for estimation and inference. A reallife data set on cotton bolls was used to demonstrate the performance of the proposed NIGLMM methodology.
Item Type:  MPRA Paper 

Original Title:  Bayesian Generalized Linear Mixed Effects Models Using NormalIndependent Distributions: Formulation and Applications 
English Title:  Bayesian Generalized Linear Mixed Effects Models Using NormalIndependent Distributions: Formulation and Applications 
Language:  English 
Keywords:  Generalized Linear Mixed Effects Models, NormalIndependent class, Normal density, Studentt, Slash density, Bayesian Method. 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling 
Item ID:  99165 
Depositing User:  Professor Waheed Babatunde Yahya 
Date Deposited:  23 Mar 2020 03:07 
Last Modified:  02 Jul 2020 22:49 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/99165 