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

Research Article

Prediction of Number of Claims using Poisson Linear Mixed Model with AR(1) random effect

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  • @INPROCEEDINGS{10.4108/eai.2-8-2019.2290464,
        author={Fia Fridayanti  Adam and Anang  Kurnia and I Gusti Putu  Purnaba and I Wayan  Mangku},
        title={Prediction of Number of Claims using Poisson Linear Mixed Model with AR(1) random effect},
        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={autoregressive process mape number of claims poisson mixed models},
        doi={10.4108/eai.2-8-2019.2290464}
    }
    
  • Fia Fridayanti Adam
    Anang Kurnia
    I Gusti Putu Purnaba
    I Wayan Mangku
    Year: 2020
    Prediction of Number of Claims using Poisson Linear Mixed Model with AR(1) random effect
    ICSA
    EAI
    DOI: 10.4108/eai.2-8-2019.2290464
Fia Fridayanti Adam1,*, Anang Kurnia2, I Gusti Putu Purnaba3, I Wayan Mangku3
  • 1: Program Vokasi, Universitas Indonesia, Depok, 16424, Indonesia
  • 2: Department of Statistics, IPB University, Bogor, 16680,Indonesia
  • 3: Department of Mathematics, IPB University, Bogor, 16680,Indonesia
*Contact email: fia@vokasi.ui.ac.id

Abstract

This study focuses on the number of claims data in an insurance company in Indonesia in 35 locations. The approach taken is a linear Poisson mixed model with two random effects. The response variable is number of claims, the fixed variable is deductibles and random effects are the area and the month of occurrence which is assumed to follow the first-order autoregressive process. Fixed and random component estimation is carried out based on MPQL while estimating component variance is using REML which the initial values are β_0= 0,β_1= 0,σ_v^2= 0.5, and σ_u^2= 1. Modeling is carried out on training data which is 75% of observations and predictions carried out with testing data which is 25% of the observations. Modeling on training and testing data produces accurate models in almost all regions included in the model. This are indicated by the MAPE values which are less than 20% in all regions.