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http://dx.doi.org/10.5351/KJAS.2021.34.5.697

Predicting claim size in the auto insurance with relative error: a panel data approach  

Park, Heungsun (Department of Statistics, Hankuk University of Foreign Studies)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.5, 2021 , pp. 697-710 More about this Journal
Abstract
Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.
Keywords
best relative error predictor; best predictor; generalized linear mixed models; random effect; percentile error; panel data; claim size;
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