Problem statement: The modeling of claims count is one of the most important topics in actuarial theory and practice. Many attempts were implemented in expanding the classes of mixed and compound distributions, especially in the distribution of exponential family, resulting in a better fit on count data. In some cases, it is proven that mixed distributions, in particular mixed Poisson and mixed negative binomial, provided better fit compared to other distributions. Approach: In this study, we introduce a new mixed negative binomial distribution by mixing the distributions of negative binomial (r,p) and Lindley (θ), where the reparameterization of p = exp(-λ) is considered. Results: The closed form and the factorial moment of the new distribution, i.e., the negative binomial-Lindley distribution, are derived. In addition, the parameters estimation for negative binomial-Lindley via the method of moments (MME) and the Maximum Likelihood Estimation (MLE) are provided. Conclusion: The application of negative binomial-Lindley distribution is carried out on two samples of insurance data. Based on the results, it is shown that the negative binomial-Lindley provides a better fit compared to the Poisson and the negative binomial for count data where the probability at zero has a large value.

Thermoplastic elastomer (TPE) has gained acceptance as third generation of polymeric materials for high voltage (HV) insulators. The interesting features showed by TPEs were found to fulfill some particular requirements for HV insulations, at least for the distribution class applications, especially at light contamination environments. Introduction of nanomaterials which widely reported to give significant improvements in the electrical, mechanical, thermal and other properties of polymer has attracted attention to the synergistic properties of combining the two complementary technologies of thermoplastic elastomers and nanocomposites. In this paper, research works on various types of TPEs having promising performance as HV insulators are thoroughly reviewed. The emergence of nanocomposites in the electrical insulation field and the important properties consideration for HV insulators as well as the processing techniques of TPE nanocomposites are also discussed. Finally, some of the past and recent developments of TPE nanocomposites focusing on the electrical insulation properties are highlighted in this article. POLYM. ENG. SCI., 58:E36–E63, 2018. © 2018 Society of Plastics Engineers

The business of insurance is based on the trust of its policyholders, who expect that their losses will be compensated should the need arise at any time. Thus, sound financial conditions constitute the most important criterion for insurance firms, as well as for takaful operators. Although the policyholder may be the most important source of insurer finance, or a debt holder from an economic point of view through premium payments, the policyholder is not well informed in assessing the financial strength or solvency of the life insurer. Various measures of the solvency of the insurer are used in the industry, such as margin of solvency (MOS), risk based capital (RBC), and claim paying ability (CPA) rating. Unfortunately, none of these can provide information to policyholders on the financial position of the insurer. This is because the MOS and RBC for each insurer is the company's and regulator's confidential information. However, for the CPA rating, it is limited to insurers who wish to be evaluated, and therefore the assessment is not comprehensive. Because of these shortcomings, this study provides a platform for policyholders to get an idea of the solvency of the insurers/takaful operators. Furthermore, this study identifies factors that affect the solvency of the insurers/takaful operators in Malaysia. Using random effects regression on panel data for 2003-2007, it is determined that investment income, total benefit paid to capital and surplus ratio, financial leverage, and liquidity are significantly related to solvency, in which the investment income has a positive relationship, while the other three have a negative relationship. From the results obtained, the policyholders/consumers can assess the insurers' financial strength through the solvency determinants of the insurers/takaful operators, even though the actual level of solvency is not known. To some extent, this information can help policyholders/consumers make smarter choices in choosing the insurers/takaful operators

There are a number of factors that cause motor vehicles to rollover. However, the impacts of roadway characteristics on rollover crashes have rarely been addressed in the literature. This study aims to apply a set of crash prediction models in order to estimate the number of rollovers as a function of road geometry, the environment, and traffic conditions. To this end, seven count-data models, including Poisson (PM), negative binomial (NB), heterogeneous negative binomial (HTNB), zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), hurdle Poisson (HP), and hurdle negative binomial (HNB) models, were developed and compared using crash data collected on 448 segments of Malaysian federal roads. The results showed that the HTNB was the best-fit model among the others to model the frequency of rollovers. The variables Light-Vehicle Traffic (LVT), horizontal curvature, access points, speed limit, and centreline median were positively associated with the crash frequency, while UnPaved Shoulder Width (UPSW) and Heavy-Vehicle Traffic (HVT) were found to have the opposite effect. The findings of this study suggest that rollovers could potentially be reduced by developing road safety countermeasures, such as access management of driveways, straightening sharp horizontal curves, widening shoulder width, better design of centreline medians, and posting lower speed limits and warning signs in areas with higher rollover tendency.

This article develops a functional form of the generalized Poisson regression model that parametrically nests the Poisson and the two well known generalized Poisson regression models (GP-1 and GP-2). The proposed model is applied on theMalaysian motor insurance claim count data.

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This aim of this study is to examine the relationship between reward administration system and organizational commitment. A survey method was employed to gather self-report questionnaires from employees in Malaysian private institutions of higher learning. The outcomes of SmartPLS path model analysis showed two important findings: first, communication was positively and significantly correlated with organizational commitment. Second, participation was positively and significantly correlated with organizational commitment. This result demonstrates that the reward administration system does act as an important predictor of organizational commitment in the studied organizations. Further, this study provides discussion, implications and conclusion.

This study introduces a new two-parameter mixed Poisson distribution, namely Poisson-Weighted Exponential (P-WE), which is obtained by mixing Poisson distribution with a new class of weighted exponential distribution. The new P-WE distribution provides a more flexible alternative for modelling over dispersed count data compared to Poisson distribution. The estimation procedures of P-WE distribution via method of moments and maximum likelihood are provided. This study also introduces P-WE regression model which can be fitted to over dispersed count data with covariates. The P-WE distribution and P-WE regression model are fitted to two sets of count data.