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Activity Number: 504
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #320375
Title: Zero-Inflated Models vs. Hurdle Models in Modeling Auto Insurance Claims in an Emerging Market: A Case Study of Nigeria
Author(s): Mary Akinyemi* and Bisola Adijat Rufai
Companies: University of Lagos and University of Lagos
Keywords: Count data ; Hurdle models ; Zero-inflated models ; Auto insurance claims ; Gini index

Count data occur naturally in a number of disciplines ranging from economics and the social sciences to finance as well as medical sciences. Most count data are plagued with over-dispersion and excess zeros making it difficult to model them with vanilla linear models. Different models have been proposed to capture this peculiarity in count data viz.: Classical models such as the generalized Poisson regression model and the negative binomial regression model have been used to model dispersed count data. Hurdle and zero-inflated models are also said to be able to capture over-dispersion and excess zeros in count data. In this paper, we compare the performance of Poisson and Negative Binomial hurdle models, zero-inflated Poisson and Negative Binomial models, classical Poisson and Negative Binomial regression models as well as the zero-inflated compound Poisson generalized linear models to modelling frequency of auto insurance claims in a typical emerging market. The model parameters are estimated using the method of maximum likelihood. The models' performances are compared based on their information criteria (AIC and BIC) and Gini index.

Authors who are presenting talks have a * after their name.

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