Occupational injury is an issue that needs to be considered in reducing the problems of disability and death in the
workplace. Healthy and high-productivity workers are one of the important factors that are important to the economic
growth of a country. This study aims to identify the trend of disability for employees covered by the Employment Injury
Scheme under the Social Security Organization Malaysia (SOCSO) for the years 2009-2013. Based on the descriptive
analysis, 80% of the total numbers of workers who suffer injury or death in the workplace are male employees. In
addition, workers aged 25 and above are more prone to injury and death associated with work. The results showed that
socio-demographic factors such as gender and age, as well as level of disability are several important factors that need
to be considered in identifying the trend of disability among employees.
This paper aims to estimate the Generalized Pareto Distribution (GPD) parameters and predicts the T-year return levels of extreme rainfall events using the Partial Duration Series (PDS) method based on the hourly rainfall data of five stations in Peninsular Malaysia. In particular, the GPD parameters are estimated using five methods namely the method of Moments (MOM), the probability weighted moments (PWM), the L-moments (LMOM), the Trimmed L-moments (TLMOM) and the Maximum Likelihood (ML) and the performance of the T-year return level of each estimation method is analyzed based on the RMSE measure obtained from Monte Carlo simulation. In addition, we suggest the weighted average model, a model which assigns the inverse variance of several methods as weights, to estimate the T-year return level. This paper contributes to the hydrological literatures in terms of three main elements. Firstly, we suggest the use
of hourly rainfall data as an alternative to provide a more detailed and valuable information for the analysis of extreme rainfall events. Secondly, this study applies five methods of parametric approach for estimating the GPD parameters and predicting the T-year return level. Finally, in this study we propose the weighted average model, a model that assigns the inverse variance of several methods as weights, for the estimation of the T-year return level.
This study suggests the regression models of Lognormal, Normal and Gamma for the construction of an insurance scoring system. Comparison between Lognormal, Normal and Gamma regression models were also carried out, and the comparison were centered upon three main elements; fitting procedures, parameter estimates and structure of scores. The main advantage of utilizing a scoring system is that the system may be used by insurers to differentiate between good and bad insureds and thus allowing the profitability of insureds to be predicted.
The objective of this study was to identify the exogenous variables of risk and investment management efficiency by using a two-stage data envelopment analysis (DEA) method. The first stage involves obtaining the efficiency scores of risk and investment management via DEA that requires only the traditional inputs and outputs. In the second stage, the Tobit regression analysis is conducted in which the efficiency score obtained from the first stage is treated as a dependent variable, while the exogenous factors are considered to be independent variables. The exogenous factors consist of operating systems, organizational form, consumer preference and size. The results showed that the mutual company as well as the takaful system demonstrate better risk management performance than their stock and conventional system counterparts. In addition, size is also a significant indicator for risk management efficiency in which the larger insurer/takaful operator exhibits better risk management performance than the smaller one. However, consumer preference is found to be insignificantly correlated with the efficiency of risk management. In contrast, with risk management, organizational form, operating system and size are not indicators of the investment management efficiency, but consumer preference is significantly and positively associated with investment management efficiency.
The aim of this paper was to identify the determinants that influence vehicle theft by applying a negative binomial regression model. The identification of these determinants is very important to policy-makers, car-makers and car owners, as they can be used to establish practical steps for preventing or at least limiting vehicle thefts. In addition, this paper also proposed a crime mapping application that allows us to identify the most risky areas for vehicle theft. The results from this study can be utilized by local authorities as well as management of internal resource planning of insurance companies in planning effective strategies to reduce vehicle theft. Indirectly, this paper has built ingenuity by combining information obtained from the database of Jabatan Perangkaan Malaysia and insurance companies to pioneer the development of location map of vehicle theft in Malaysia.