Quantile regression model estimates the relationship between the quantile of a response distribution and the regression parameters, and has been developed for linear models with continuous responses. In this paper, we apply Bayesian quantile regression model for the Malaysian motor insurance claim count data to study the effects of change in the estimates of regression parameters (or the rating factors) on the magnitude of the response variable (or the claim count). We also compare the results of quantile regression models from the Bayesian and frequentist approaches and the results of mean regression models from the Poisson and negative binomial. Comparison from Poisson and Bayesian quantile regression models shows that the effects of vehicle year decrease as the quantile increases, suggesting that the rating factor has lower risk for higher claim counts. On the other hand, the effects of vehicle type increase as the quantile increases, indicating that the rating factor has higher risk for higher claim counts. (C) 2015 Elsevier B.V. All rights reserved.

The recognition of intraspecific variation could enhance knowledge and understanding on the population divergence that might be resulted from different geographical areas. To study the possible effect derived from different locations, a morphometric study of the red stripe weevils, Rhynchophoms vulneratus from different localities in Malaysia was conducted using field and voucher specimens. A total of twenty-three morphological characters were examined from 108 individuals of R. vulneratus representing population of Kota Samarahan, Mukah and central of Peninsular Malaysia. The data were subjected to univariate one-way single factor analysis of variance (ANOVA) and analysed in factor analysis using SPSS version 22.0 software. Univariate ANOVA showed that all tested variables were significantly different (p<0.05) except for mesocoxal distance (MSD), meanwhile from the factor analysis, the first three factors with eigenvalues greater than 1.0 were successfully extracted, resulting in a high variation of 82.687%. For factor 1, 39.213% of total variation was recorded, factor 2 accounted for 34.096% and factor 3 contributing to 9.377%, respectively. The mixed plotting among the twenty-three morphological characters suggests a strong correlation among the parameters examined and further statistical analysis should be conducted to include environmental factors such as habitat types, food availability and predation effect.