Research Article
Prediction of Number of Claims using Poisson Linear Mixed Model with AR(1) random effect
@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
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 β0= 0,β1= 0,σv^2= 0.5, and σu^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.