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Bootstrap Approaches to Autoregressive Model on Exchange Rates Currency

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Open Journal of Statistics 2016年第6期6卷 1010-1024页

作者： Muhamad Safiih Lola Anthea David nurul hila zainuddin School of Informatics and Applied Mathematics Universiti Malaysia Terengganu Kuala Nerus Malaysia

The use of historical data is important in making the predictions, for instance in the exchange rate. However, in the construction of a model, extreme data or dirtiness of data is inevitable. In this study, AR model is used with the exchange rate historical data (January 2007 until December 2007) for USD/MYR and is divided into 1-, 3- and 6-horizontal months respectively. Since the presence of extreme data will affect the accuracy of the results obtained in a prediction. Therefore, to obtain a more accurate prediction results, the bootstrap approach was implemented by hybrid with AR model coins as the Bootstrap Autoregressive model (BAR). The effectiveness of the proposed model is investigated by comparing the existing and the proposed model through the statistical performance methods which are RMSE, MAE and MAD. The comparison involves 1%, 5% and 10% for each horizontal month. The results showed that the BAR model performed better than the AR model in terms of sensitivity to extreme data, the accuracy of forecasting models, efficiency and predictability of the model prediction. In conclusion, bootstrap method can alleviate the sensitivity of the model to the extreme data, thereby improving the accuracy of forecasting model which also have high prediction efficiency and that can increase the predictability of the model.

关键词： Autoregressive Model Outliers Bootstrap Robust

Sample Selection Model with Bootstrap (BPSSM) Approach: Case Study of the Malaysian Population and Family Survey

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Open Journal of Statistics 2016年第5期6卷 741-748页

作者： Muhamad Safiih Lola Wan Saliha Wan Alwi nurul hila zainuddin School of Informatics and Applied Mathematics Universiti Malaysia Terengganu Kuala Terengganu Terengganu Malaysia

Heckman Sampel Selection Model (PSSM) has been adopted widely in the study of labour work. This model contains exogenous, endogenous and standard error variables. However, this model is constantly exposed to high inaccuracy of estimation result. Therefore, to obtain an accurate and precise estimation, the bootstrap approach is introduced. The bootstrap approach will be hybrid with PSSM model known as BPSSM to achieve estimation result that is more precise. Then, the BPSSM is applied to Malaysian Population and Family Survey 1994 (MPFS-1994) data. The results showed that BPSSM provide a smaller standard error and shorter confidence intervals.

关键词： Sampel Selection Bootstrap Standard Error Confidence Intervals

Forecasting towards Planning and Sustainable Development Based on a System Dynamic Approach: A Case Study of the Setiu District, State of Terengganu, Malaysia

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Open Journal of Statistics 2016年第5期6卷 931-950页

作者： Muhamad Safiih Lola Mohd Noor Afiq Ramlee Hanani Isa Mhd Ikhwanuddin Abdullah Mohd Fadli Hussin nurul hila zainuddin Mohd Naeim Rahman School of Informatics and Applied Mathematics University Malaysia Terengganu Kuala Terengganu Malaysia Institute Aquaculture Tropical University Malaysia Terengganu Kuala Terengganu Malaysia

In this paper, a system dynamics approach is used instead of the traditional approaches to stimulate, forecast and analyze the economic effects of an existing policy practice in Setiu Wetland. As a part of Setiu district that uphold tradition in fishery and maritime based industry, Setiu Wetland area seems to be left behind in terms of economic and livelihood. Generally, Setiu development policy consists of five sub-system including population growth, economic, residential, transportation and sub-urban sprawl. Due to their widespread population distribution, Setiu Wetland receives low urban-related progress. Hence, a forecast of 30 years from 2016 to 2046 providing a necessary insight for potential development of the Setiu Wetland region, to simulate its environment, identify gaps, propose suitable land model towards Setiu Minapolitan area (Peri-urban area) and suggest directions for future studies particularly in economic and livelihood for local authorities to develop with.

关键词： System Dynamics Setiu Wetland Minapolita Economic and Livelihood Policy Practice

Improved the Prediction of Multiple Linear Regression Model Performance Using the Hybrid Approach: A Case Study of Chlorophyll-a at the Offshore Kuala Terengganu, Terengganu

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Open Journal of Statistics 2016年第5期6卷 789-804页

作者： Muhamad Safiih Lola Mohd Noor Afiq Ramlee G. Sugan Gunalan nurul hila zainuddin Razak Zakariya MdSuffian Idris Idham Khalil School of Informatics and Applied Mathematics University Malaysia Terengganu Kuala Terengganu Malaysia School of Science Marin University Malaysia Terengganu Kuala Terengganu Malaysia

Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and physical processes and interaction among them may affect the model performance drastically. Thus, to overcome this problem as well as to improve the strength of MLR, we proposed a hybrid approach, i.e., an Artificial Neural Network to the MLR coins as Artificial Neural Network-Multiple Linear Regression (ANN-MLR). To investigate the performance of the proposed model, we compared Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and proposed hybrid Artificial Neural Network and Multiple Linear Regression (ANN-MLR) in the prediction of chlorophyll-a (chl-a) concentration by statistical measurement which are MSE and MAE. Achieving our objectives of study, we used 4 parameters, i.e. temperature (°C), pH, salinity (ppt), DO (ppm) at the Offshore Kuala Terengganu, Terengganu, Malaysia. The results showed that our proposed model can improve the performance of the model as compared to ANN and MLR due to small errors generated, error reduced, and increased the correlation coefficient for all parameters in both MSE and MAE, respectively. Thus, this result indicated that our proposed model is efficient, precise and almost perfect correlation as compared to ANN and MLR.

关键词： Multi Linear Regression Artificial Neural Network ANN-MLR Chlorophyll-a Correlation

An Eigenspace Method for Detecting Space-Time Disease Clusters with Unknown Population-Data

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Computers, Materials & Continua 2022年第1期70卷 1945-1953页

作者： Sami Ullah nurul Hidayah Mohd Nor Hanita Daud Nooraini zainuddin Hadi Fanaee-T Alamgir Khalil Department of Fundamental&Applied Sciences Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Center for Applied Intelligent Systems Research(CAISR) Halmstad UniversityHalmstadSE-30118Sweden Department of Statistics University of PeshawarPakistan

Space-time disease cluster detection assists in conducting disease surveillance and implementing control *** state-of-the-art method for this kind of problem is the Space-time Scan Statistics(SaTScan)which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson orGaussian *** this problem,an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric ***,it is based on the population counts data which are not always available in the least developed *** addition,the population counts are difficult to approximate for some surveillance data such as emergency department visits and over-the-counter drug sales,where the catchment area for each hospital/pharmacy is *** extend the population-based Multi-EigenSpot method to approximate the potential disease clusters from the observed/reported disease counts only with no need for the population *** proposed adaptation uses an estimator of expected disease count that does not depend on the population *** proposed method was evaluated on the real-world dataset and the results were compared with the population-based methods:Multi-EigenSpot and *** result shows that the proposed adaptation is effective in approximating the important outputs of the population-based methods.

关键词： Space-time disease clusters Eigenspace method nontraditional data sources nonparametric methods