Research Article
Analysis of Bayesian Generalized Linear Models on the Number of Tuberculosis Patients in Indonesia with R
@INPROCEEDINGS{10.4108/eai.2-8-2019.2290479, author={Femmy Diwidian and Anang Kurnia and Kusman Sadik}, title={Analysis of Bayesian Generalized Linear Models on the Number of Tuberculosis Patients in Indonesia with R}, proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia}, publisher={EAI}, proceedings_a={ICSA}, year={2020}, month={1}, keywords={generalized linear model's (glm) frequentis method bayesian glm method}, doi={10.4108/eai.2-8-2019.2290479} }- Femmy Diwidian
Anang Kurnia
Kusman Sadik
Year: 2020
Analysis of Bayesian Generalized Linear Models on the Number of Tuberculosis Patients in Indonesia with R
ICSA
EAI
DOI: 10.4108/eai.2-8-2019.2290479
Abstract
Generalized Linear Models (GLM) is an extension of the linear regression model that aims to determine the causal relationship, the effect of independent variables on the dependent variable where the response variable is a member of the exponential family. In general, estimating parameters on GLM can be divided into two approaches, namely the frequentist method and the Bayesian GLM method. In this study, both approaches will be used to analyze the number of people suffering from tuberculosis in 34 provinces in Indonesia. The data used is based on 2018 Indonesia Health Profile Data and Information published by the Ministry of Health of the Republic of Indonesia in 2018. Based on the best model test criteria, this study provides results that the frequentist approach to GLM is better in matching the number of people suffering from tuberculosis in Indonesia compared to use Bayesian GLM.
