Norazan Mohamed Ramli

MEMECYLON SPECIES: A REVIEW OF TRADITIONAL INFORMATION AND TAXONOMIC DESCRIPTION

The relationship between genes in gene set analysis in microarray data is analyzed using Hotelling’s T 2 but the test cannot be applied when the number of samples is larger than the number of variables which is uncommon in the microarray. Thus, in this study, we proposed shrinkage approaches to estimating the covariance matrix in Hotelling’s T 2 particularly to cater high dimensionality problem in microarray data. Three shrinkage covariance methods were proposed in this study and are referred as Shrink A, Shrink B and Shrink C. The analysis of the three proposed shrinkage methods was compared with the Regularized Covariance Matrix Approach and Kong’s Principal Component Analysis. The performances of the proposed methods were assessed using several cases of simulated data sets. In many cases, the Shrink A method performed the best, followed by the Shrink C and RCMAT methods. In contrast, both the Shrink B and KPCA methods showed relatively poor results. The study contributes to an establishment of modified multivariate approach to differential gene expression analysis and expected to be applied in other areas with similar data characteristics.

Geometric median for missing rainfall data imputation

Missing data is a common problem faced by researchers in environmental studies. Environmental data, particularly, rainfall data are highly vulnerable to be missed, which is due to several reasons, such as malfunction instrument, incorrect measurements, and relocation of stations. Rainfall data are also affected by the presence of outliers due to the temporal and spatial variability of rainfall measurements. These problems may harm the quality of rainfall data and subsequently, produce inaccuracy in the results of analysis. Thus, this study is aimed to propose an imputation method that is robust towards the presence of outliers for treating the missing rainfall data. Geometric median was applied to estimate the missing values based on the available rainfall data from neighbouring stations. The method was compared with several conventional methods, such as normal ratio and inverse distance weighting methods, in order to evaluate its performance. Thirteen rainfall stations in Peninsular Malaysia were selecte...

The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

Determining the best forecasting method to estimate unitary charges price indexes of PFI data in central region Peninsular Malaysia

The concept of Private Financial Initiative (PFI) has been implemented by many developed countries as an innovative way for the governments to improve future public service delivery and infrastructure procurement. However, the idea is just about to germinate in Malaysia and its success is still vague. The major phase that needs to be given main attention in this agenda is value for money whereby optimum efficiency and effectiveness of each expense is attained. Therefore, at the early stage of this study, estimating unitary charges or materials price indexes in each region in Malaysia was the key objective. This particular study aims to discover the best forecasting method to estimate unitary charges price indexes in construction industry by different regions in the central region of Peninsular Malaysia (Selangor, Federal Territory of Kuala Lumpur, Negeri Sembilan, and Melaka). The unitary charges indexes data used were from year 2002 to 2011 monthly data of different states in the central region Peninsula...

Estimating cement price index by regions in Peninsular Malaysia

Malaysia is moving forward towards a developed country by the year 2020. Therefore, implementation of Private Financial Initiative (PFI) in Malaysia is really needed in order to improve the delivery and quality of infrastructure facilities and public services in this nation. The success of this program can only be made possible by healthy participation from both public and private sectors in Malaysia. The most essential aspect that needs to be fulfilled in this program is value for money (VFM) whereby maximum efficiency and effectiveness of every purchase is attained. Hence, at the preliminary stage of this study, estimating materials price index in Malaysia is the main objective. This particular paper aims to discover the best forecasting method to estimate cement price index by different regions in Peninsular Malaysia since cement is the main material used in construction industry. Cement index data used were from year 2005 to 2011 monthly data of different regions in Peninsular Malaysia. It was found that Backpropagation Neural Network with linear transfer function produced the most accurate and reliable results for estimating cement price index in every region in Malaysia. The neural network models selection were based on the Root Mean Squared Errors (RMSE), where the values were approximately zero errors and highly significant at p<;0.01. Therefore, artificial neural network is sufficient to forecast cement price index in Malaysia. The estimated price indexes of cement will contribute significantly to value for money in PFI and soon towards Malaysian economical growth.

Outlier detection in logistic regression and its application in medical data analysis

The application of logistic regression is widely used in medical research. The detection of outliers has become an essential part of logistic regression. It is often observed outliers have a considerable influence on the analysis results, which may lead the study to the wrong conclusions. Many procedures for the identification of outliers in logistic regression are available in the literature. In this paper, four methods for outlier detection have been investigated and compared through numerical examples.

Determining the best forecasting model of cement price index in Malaysia

Malaysia is aiming towards a developed country by the year 2020. Therefore, implementation of Private Financial Initiative (PFI) in Malaysia is needed as a procurement method to improve the delivery and quality of infrastructure facilities and public services in this country. The most essential aspect that needs to be fulfilled in this program is value for money (VFM) whereby maximum efficiency and effectiveness of every purchase is attained. Hence, at the preliminary stage of this study, estimating materials price index in Malaysia is the main objective. This particular paper aims to discover the best forecasting method to estimate cement price index by different regions in Malaysia since cement is the main material used in construction industry. Cement index data used were from year 2005 to 2011 monthly data of different regions in Peninsular Malaysia, and year 2003 to 2011 monthly data in both Sabah and Sarawak. It was found that Backpropagation Neural Network (BPNN) with linear transfer function produced the most accurate and reliable results for estimating cement price index in every region in Malaysia. The neural network models selection were based on the Root Mean Squared Errors (RMSE), where the values were approximately zero errors and highly significant at p<0.01. Therefore, artificial neural network is sufficient to forecast cement price index in Malaysia. The estimated price indexes of cement will contribute significantly to value for money in PFI and soon towards Malaysian economical growth.

Prognostic Factors for Rheumatics Heart Disease After Mitral Valve Repair Surgery Using Cox Proportional Hazard Model

Mitral valve repair surgery is associated with the improvement of prognostic outcomes. However, it is unclear whether mitral valve repair surgery alone is sufficient or requires a combination of repair with other repair procedures to improve the survival rate among rheumatic heart disease (RHD) patients. The aim of the study is to determine the risk of death among RHD patients after mitral valve repair surgery. A cohort-retrospective study was conducted among 771 RHD patients. The overall 10-year survival rate after mitral valve repair surgery was 93.7%. This study employed cox proportional hazard (PH) model to determine the significant prognostic factors among RHD survival patients after mitral valve repair surgery. Prognosis factors are considered statistically significant when Wald statistic produces P-value less than 0.05. A multivariate model analysis indicates that only five prognostic factors are statistically significant, namely hypertension (HPT), emergency status diagnosed at intra-operation, combination mitral valve repair surgery, higher coronary pulmonary bypass (CPB), longer number of days of hospital stay (HOSP), and redo surgery diagnosed at post-operation. The study concludes that the combination mitral valve repair surgery with other surgical procedures improved survival rates of RHD patients with given good surgical outcomes.

Authenticating ANN-NAR and ANN-NARMA Models Utilizing Bootstrap Techniques

Neural system procedures have a colossal reputation in the space of gauging. In any case, there is yet to be a sure strategy that can well accept the last model of the neural system time arrangement demonstrating. Thus, this paper propose a way to deal with accepting the said displaying utilizing time arrangement square bootstrap. This straightforward technique is different compared to the traditional piece bootstrap of time-arrangement based, where it was composed by making utilization of every information set in the information apportioning procedure of neural system demonstrating; preparing set, testing set and approval set. At this point, every information set was separated into two little squares, called the odd and even pieces (non-covering pieces). At that point, from every piece, an arbitrary inspecting with substitution in a rising structure was made, and these duplicated tests can be named as odd-even square bootstrap tests. In time, the examples were executed in the neural system preparing for last voted expectation yield. The proposed strategy was forced on both manufactured neural system time arrangement models, which were nonlinear autoregressive (NAR) and nonlinear autoregressive moving normal (NARMA). In this study, three changing genuine modern month to month information of Malaysian development materials value records from January 1980 to December 2012 were utilized. It was found that the suggested bootstrapped neural system time arrangement models beat the first neural system time arrangement models.

Normal ratio in multiple imputation based on bootstrapped sample for rainfall data with missingness

The existence of missing values in rainfall data series is inevitably affects the quality of the data. This problem will influence the results of analysis and subsequently provide imprecise information to the hydrological and meteorological management. A practical and reliable approach is needed in developing estimation methods to impute the missing values. Single imputation is the most commonly used approach for missing values, but, it encounters with the limitation of not considering the uncertainty and natural variability in missing data imputation. Thus, this study has proposed multiple imputation approach based on bootstrap samples in order to overcome the limitation of single imputation approach. Three normal ratio estimation methods are implemented using the proposed approach. The performances of the estimation methods are evaluated at six different levels of missingness. Complete 40 years daily rainfall data from four meteorology stations were considered for the analysis purpose with Johor Bahru station was selected as the target station. The results of the proposed approach were compared to the results obtained from single imputation approach and the widely known built in software for multiple imputation, Amelia II package, in assessing the performance of proposed approach. The results showed that all estimation methods that implemented using proposed approach provided the most accurate estimation results at all percentages of missingness. This proves the advantage of adaption of variability and uncertainty element in the proposed approach in estimating the missing rainfall data at the area of the current study.