Quantile Regression Modeling for Malaysian Automobile Insurance Premium Data

Quantile regression is a robust regression to outliers compared to mean regression models. Traditional mean regression models like Generalized Linear Model (GLM) are not able to capture the entire distribution of premium data. In this paper we demonstrate how a quantile regression approach can be used to model net premium data to study the effects of change in the estimates of regression parameters (rating classes) on the magnitude of response variable (pure premium). We then compare the results of quantile regression model with Gamma regression model. The results from (pantile regression show that some rating classes increase as (pantile increases and some decrease with decreasing quantile. Further, we found that the confidence interval of median regression (tau = 0.5) is always smaller than Gamma regression in all risk factors.