Open AccessModelling of the number of malarias suffers in Indonesia using Bayesian generalized linear models
Author(s) -
Vera Maya Santi,
Anang Kurnia,
Kusman Sadik
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1402/7/077091
Subject(s) - generalized linear model , akaike information criterion , statistics , mathematics , deviance information criterion , exponential family , econometrics , bayesian information criterion , covariate , bayesian linear regression , bayesian probability , frequentist inference , poisson distribution , regression analysis , linear regression , linear model , bayesian inference
Generalized Linear Models (GLM) has been used for modelling various types of data where the distribution of response variables is an exponential family. Common examples include those for binomial and Poisson response data. The GLM regression model determines the structure of the explanatory variable or covariate information, where the link function specifically determines the relationship between the regression model and the expected value of the observation. Bayesian techniques can now be applied to complex modelling problems where they could not have been applied previously. This method is a simpler model than traditional frequentist techniques. Estimating the regression model parameters is done by using Bayesian GLM. In this paper, we study conducted modelling for the number of dengue sufferers in Indonesia using the Bayesian GLM approach with several prior distributions. There are 6 independent variables that have a significant effect on the regression model, that is population density, Gini ratio, proper sanitation access, healthy zoning, integrated control and total sanitation. Based on Akaike Information Criterion (AIC) and standard error, the Bayesian GLM estimation results for Cauchy and Normal prior distribution will converge to the same value as that obtained by GLM.