Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia

Research Article

Analysis of Bayesian Generalized Linear Models on the Number of Tuberculosis Patients in Indonesia with R

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  • @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
Femmy Diwidian1, Anang Kurnia2,*, Kusman Sadik2
  • 1: Mathematics Education Study Program, UIN Syarif Hidayatullah Jakarta, 15419, Indonesia
  • 2: Department of Statistics, Bogor Agricultural University, Bogor, 16680, Indonesia
*Contact email: anangk@apps.ipb.ac.id

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.