MCMC and GLMs for estimating regression parameters: Evidence from non-life Egyptian insurance sector

Purpose – The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM) and the second technique is the Markov Chain Monte Carlo (MCMC) method. Design/methodology/approach – In this paper, th...

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Bibliographic Details
Main Authors: Mahmoud ELsayed, Amr Soliman
Format: Article
Language:English
Published: Emerald Publishing 2019-10-01
Series:Journal of Humanities and Applied Social Sciences
Subjects:
Online Access:https://www.emerald.com/insight/content/doi/10.1108/JHASS-08-2019-0028/full/pdf?title=mcmc-and-glms-for-estimating-regression-parameters-evidence-from-non-life-egyptian-insurance-sector
Description
Summary:Purpose – The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM) and the second technique is the Markov Chain Monte Carlo (MCMC) method. Design/methodology/approach – In this paper, the authors adopted the incurred claims of Egyptian non-life insurance market as a dependent variable during a 10-year period. MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate the parameters of interest. However, the authors used the R package to estimate the parameters of the linear regression using the above techniques. Findings – These procedures will guide the decision-maker for estimating the reserve and set proper investment strategy. Originality/value – In this paper, the authors will estimate the parameters of a linear regression model using MCMC method via R package. Furthermore, MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate parameters to predict future claims. In the same line, these procedures will guide the decision-maker for estimating the reserve and set proper investment strategy.
ISSN:2632-279X