This study is aimed to estimate missing rainfall data by dividing the analysis into three different percentages namely 5%, 10% and 20% in order to represent various cases of missing data. In practice, spatial interpolation methods are chosen at the first place to estimate missing data. These methods include normal ratio (NR), arithmetic average (AA), coefficient of correlation (CC) and inverse distance (ID) weighting methods. The methods consider the distance between the target and the neighbouring stations as well as the correlations between them. Alternative method for solving missing data is an imputation method. Imputation is a process of replacing missing data with substituted values. A once-common method of imputation is single-imputation method, which allows parameter estimation. However, the single imputation method ignored the estimation of variability which leads to the underestimation of standard errors and confidence intervals. To overcome underestimation problem, multiple imputations method is used, where each missing value is estimated with a distribution of imputations that reflect the uncertainty about the missing data. In this study, comparison of spatial interpolation methods and multiple imputations method are presented to estimate missing rainfall data. The performance of the estimation methods used are assessed using the similarity index (S-index), mean absolute error (MAE) and coefficient of correlation (R).

Gold has been regarded as a valuable precious metal and the most popular commodity as a healthy return investment. Hence, the analysis and prediction of gold price become very significant to investors. This study is a preliminary analysis on gold price and its volatility that focuses on the performance of hybrid Box-Jenkins models together with GARCH in analyzing and forecasting gold price. The Box-Cox formula is used as the data transformation method due to its potential best practice in normalizing data, stabilizing variance and reduces heteroscedasticity using 41-year daily gold price data series starting 2 nd January 1973. Our study indicates that the proposed hybrid model ARIMA-GARCH with t-innovation can be a new potential approach in forecasting gold price. This finding proves the strength of GARCH in handling volatility in the gold price as well as overcomes the non-linear limitation in the Box-Jenkins modeling.

Eng.pdf) highlights the importance, in a globalised world, of producing future intellectual, social and human capital for Malaysia. It is paramount to note that adolescents are the future human capital of Malaysia. Literature indicates adolescents are confronted with an environment that is rapidly changing and the time spent on nonschool-related activities has negatively impacted academic performance. This research investigated the contribution of time use among students to academic achievement. The sample of secondary school students was stratified to ensure balance of gender, school type and grade level. Correlation analyses were conducted to determine the associations among the variables identified in the study. Participants completed the Daily Record of How I Use My Time each day for seven consecutive days. Cooperating schools provided copies of the academic transcript of each participant. This information was used to establish the overall secondary school academic achievement of each participant. The data collected were subjected to bivariate comparison of means and t-test comparisons of means to yield correlations among the target variables. The findings of this research support those of some previous studies and contradict those of other studies, many conducted with non-Malaysian populations. In sum, this study may help to develop a conceptual framework for guiding efforts to improve academic performance, as it relates to time use, as a contribution to accomplishing the national agenda of Malaysia.

In meteorological and hydrological researches, missing rainfall data has always been one of the most challenging problems which need to be faced by the researchers. The problems of missing rainfall data are due to the wrong technique used when measuring the rainfall, relocation of the rain station and malfunctioned of instrument. Finding the suitable method to solve missing data problem is very critical before going to the next level of data analysis. Most researchers used the spatial interpolation method to estimate the missing rainfall data at a particular target station which is based on the available rainfall data at their neighboring stations. The spatial interpolation method is one of the traditional weighting factors which also consider the correlation between the stations. This study uses the modified of spatial interpolation weighting methods to estimate the missing rainfall data in Pahang and only assume that the particular target station has the missing value. A new modified method of normal ratio and inverse distance weighting with correlation is proposed by abbreviated by NRIDC. The performance of the modified spatial interpolation weighting methods used are assessed using the similarity index (S-index), mean absolute error (MAE) and coefficient of correlation (R) for different percentage of missing values (5%-30%).

No abstract