An analysis of the effect of English proficiency towards students' academic performance in university of Malaysia Sabah

Students are the main assets of universities. Students’ academic performance has been the top priority for themselves, educators, researchers, government, and parents. There have been many studies that sought to investigate factors that influence students’ academic performance and they discover that hard work, discipline, class attendance, self-motivation, previous schooling and family’s income affect students’ academic performance, and these seem to have significant effects on the students’ final Cumulative Grade Point Average, CGPA. This study aims to investigate whether students’ English language results and their entry results during admission are also factors that influence their final Cumulative Grade Point Average, CGPA. Multiple linear regression was the analysis used to determine the effect of English proficiency towards students’ academic performance. Thus, the results will show the factors that significantly contribute to the students’ academic performance

Discussion of "Statistical Modeling of Spatial Extremes" by A. C. Davison, S. A. Padoan and M. Ribatet

Discussion of "Statistical Modeling of Spatial Extremes" by A. C. Davison, S. A. Padoan and M. Ribatet [arXiv:1208.3378].Comment: Published in at http://dx.doi.org/10.1214/12-STS376B the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modeling the Extreme Rainfall Data of Several Sites in Sabah using Sandwich Estimator

When the extreme data were obtained from several sites in a region, spatial extreme analysis is always been considered. In this paper, we model the annual maximum rainfall data by using generalized extreme value distribution. We fit the model independently for each site to prevent extreme value complex modeling. However, it also cause the statistical assumption of dependency between sites been violated. Therefore, we applied the sandwich estimator to correct the variance of the model. We also consider an analysis of small sample sizes of the observed data. The method of penalized maximum likelihood estimation was carried out to improve the inference of the model. In the end, the return levels of the annual maximum rainfall data were computed by using the corrected model

Modelling Extreme Rainfall Using Adjusted Sandwich Estimator

The Generalized Extreme Value (GEV) distribution is often used to describe the frequency of occurrence of extreme rainfall. Modelling the extreme event using the independent Generalized Extreme Value to spatial data fails to account the behaviour of dependency data. However, the wrong statistical assumption by this marginal approach can be adjusted using sandwich estimator. In this paper, we used the conventional method of the marginal fitting of generalized extreme value distribution to the extreme rainfall then corrected the standard error to account for inter-site dependence. We also applied the penalized maximum likelihood to improve the generalized parameter estimations. A case study of annual maximum rainfall from several stations at western Sabah is studied, and the results suggest that the variances were found to be greater than the standard error in the marginal estimation as the inter-site dependence being considered

Parameter estimations of the generalized extreme value distributions for small sample size

The standard method of the maximum likelihood has poor performance in GEV parameter estimates for small sample data. This study aims to explore the Generalized Extreme Value (GEV) parameter estimation using several methods focusing on small sample size of an extreme event. We conducted simulation study to illustrate the performance of different methods such as the Maximum Likelihood (MLE), probability weighted moment (PWM) and the penalized likelihood method (PMLE) in estimating the GEV parameters. Based on the simulation results, we then applied the superior method in modelling the annual maximum stream flow in Sabah. The result of the simulation study shows that the PMLE gives better estimate compared to MLE and PMW as it has small bias and root mean square errors, RMSE. For an application, we can then compute the estimate of return level of river flow in Sabah

Analysis of wage distribution in Malaysia

Labor force demand in Malaysia has grown significantly over the decades since the independence era. Changes in economic structure have led to changes in labor force utilization. There are hundreds of studies had been done to investigate the determinants of wages, including human capital factor, demographic factor as well as job characteristic. The objective of this paper is to examine the determinant of wages in Malaysia using the Salaries and Wages Survey, 2016 conducted by the Department of Statistics Malaysia. Based on the analysis of mean differences, the average wage is significantly different for all variables, including age, ethnicity, marital status, education level and occupation. Even in the analysis of the distribution for men and women, the difference in average wage is also identified for each occupation category and sector of the industry

An Improvement of Computing Newton’s Direction for Finding Unconstrained Minimizer for Large-Scale Problems with an Arrowhead Hessian Matrix

In large-scale problems, classical Newton’s method requires solving a large linear system of equations resulting from determining the Newton direction. This process often related as a very complicated process, and it requires a lot of computation (either in time calculation or memory requirement per iteration). Thus to avoid this problem, we proposed an improved way to calculate the Newton direction using an Accelerated Overrelaxation (AOR) point iterative method with two different parameters. To check the performance of our proposed Newton’s direction, we used the Newton method with AOR iteration for solving unconstrained optimization problems with its Hessian is in arrowhead form and compared it with a combination of the Newton method with Gauss-Seidel (GS) iteration and the Newton method with Successive Over Relaxation (SOR) iteration. Finally, comparison results show that our proposed technique is significantly more efficient and more reliable than reference methods

A step towards efficient inference for trends in UK extreme temperatures through distributional linkage between observations and climate model data

The aim of this paper is to set out a strategy for improving the inference for statistical models for the distribution of annual maxima observed temperature data, with a particular focus on past and future trend estimation. The observed data are on a 25 km grid over the UK. The method involves developing a distributional linkage with models for annual maxima temperatures from an ensemble of regional and global climate numerical models. This formulation enables additional information to be incorporated through the longer records, stronger climate change signals, replications over the ensemble and spatial pooling of information over sites. We find evidence for a common trend between the observed data and the average trend over the ensemble with very limited spatial variation in the trends over the UK. The proposed model, that accounts for all the sources of uncertainty, requires a very high dimensional parametric fit, so we develop an operational strategy based on simplifying assumptions and discuss what is required to remove these restrictions. With such simplifications we demonstrate more than an order of magnitude reduction in the local response of extreme temperatures to global mean temperature changes

Approximate analytical solutions of nonlinear hyperbolic partial differential equation

The Multistep Modified Reduced Differential Transform Method (MMRDTM) is proposed and implemented in this study to obtain solutions of hyperbolic partial differential equations. We examine at the nonlinear Schrodinger equation (NLSE). Prior to implementing the multistep strategy, we switched the nonlinear term in the NLSE with the corresponding Adomian polynomials using the proposed technique. As a result, we can acquire solutions for the NLSE in a simpler and less difficult manner. Furthermore, the solutions can be estimated more precisely over a longer time period. We studied the NLS equation and graphed the features of this solution to show the strength and accurateness of the proposed technique

An alternative approach for finding Newton's direction in solving large-scale unconstrained optimization for problems with an arrowhead Hessian matrix

In this paper, we proposed an alternative way to find the Newton direction in solving large-scale unconstrained optimization problems where the Hessian of the Newton direction is an arrowhead matrix. The alternative approach is a two-point Explicit Group GaussSeidel (2EGGS) block iterative method. To check the validity of our proposed Newton’s direction, we combined the Newton method with 2EGGS iteration for solving unconstrained optimization problems and compared it with a combination of the Newton method with Gauss-Seidel (GS) point iteration and the Newton method with Jacobi point iteration. The numerical experiments are carried out using three different artificial test problems with its Hessian in the form of an arrowhead matrix. In conclusion, the numerical results showed that our proposed method is more superior than the reference method in term of the number of inner iterations and the execution time