Analysis of Vehicle Theft: A Case Study in Malaysia using Functional Forms of Negative Binomial Regression Models

Abstract

This paper proposes the application of the two well known models of negative binomial regression, namely the NB-1 and the NB-2, and the functional form of negative binomial regression, namely the NB-P, for the analysis of vehicle theft crime. The advantage of using the NB-P is that it parametrically nests both the NB-1 and the NB-2, and allows statistical tests of the NB-1 and the NB-2 models against a more general alternative. In this study, the functional forms of negative binomial regression model were fitted to vehicle theft crime data obtained and compiled from ten insurance companies in Malaysia. The results of this study indicate that the risks of vehicle theft are higher for vehicles above eight years, vehicles above 1800 c.c., local vehicles and vehicles located in the central area of Malaysia.

Poisson-Weighted Exponential Univariate Version and Regression Model With Applications

“…For handling over dispersion, several regression models such as NB and GP have been suggested. Several parameterizations have been performed for NB regression models and the two well known models, referred asNB-1 and NB-2 in Greene (2008), have been developed and applied (Cameron and Trivedi, 1986;Lawless, 1987;Ismail and Jemain, 2007;Zulkifli et al, 2013). Several parameterizations have also been performed for GP regression models and the two well known models, referred as GP-1 and GP-2 in Yang et al (2009), have been developed and applied (Consul, 1989;Ismail and Jemain, 2007;Ismail and Zamani, 2013).…”

Statistical Analysis of Vehicle Theft Crime in Peninsular Malaysia using Negative Binomial Regression Model

“…Brockman and Wright (1992), Ismail and Jemain (2007) and McCullagh and Nelder (1989) suggested a quasiPoisson regression model to accommodate overdispersion in claim count and count data in other areas. The negative binomial regression model has been fitted for overdispersed claim count and count data in other areas by Ismail and Jemain (2007), Lawless (1987), McCullagh and Nelder (1989) and Zulkifli et al (2013). Generalized Poisson regression models have been applied to overdispersed and underdispersed count data by Consul (1989), Consul and Famoye (1992), Wang and Famoye (1997), Zamani and Ismail (2012) and Zamani and Ismail (2014).…”