In this paper, four types of circular probability distribution were used to evaluate which circular probability distribution gives the best fitting for southwesterly Malaysian wind direction data, namely circular uniform distribution, von Mises distribution, wrapped-normal distribution and wrapped-Cauchy distribution. The four locations chosen were Alor Setar, Langkawi, Melaka and Senai. Two performance indicators or goodness of fit tests which are mean circular distance and chord length were used to test which distribution give the best fitting.
Missing values have always been a problem in analysis. Most exclude the missing values from the analyses which may lead to biased parameter estimates. Some imputations methods are considered in this paper in which simulation study is conducted to compare three methods of imputation namely mean substitution, hot deck and expectation maximization (EM) imputation. The EM imputation is found to be superior especially when the percentage of missing values is high as it constantly gives low RMSE as compared with other two methods. The EM imputation method is then applied to the PM10 concentrations data set for the southwest and northeast monsoons in Petaling Jaya and Seberang Perai, Malaysia which has missing values. Four types of distributions, namely the Weibull, lognormal, gamma and Gumbel distribution are considered to describe the PM10 concentrations. The Weibull distribution gives the best fit for the southwest monsoon data for Petaling Jaya. The lognormal distribution outperformed the others in describing the southwest monsoon in Seberang Perai. Meanwhile, for the northeast monsoon in both locations, gamma distribution is the best distribution to describe the data.
Missing value problem is common when analysing quantitative data. With the rapid growth of computing capabilities, advanced methods in particular those based on maximum likelihood estimation has been suggested to best handle the missing values problem. In this paper, two modern imputing approaches namely expectation-maximization (EM) and expectation-maximization with bootstrapping (EMB) are proposed in this paper for two kinds of linear functional relationship (LFRM) models, namely LFRM1 for full model and LFRM2 for linear functional relationship model when slope parameter is estimated using a nonparametric approach. The performance of EM and EMB are measured using mean absolute error, root-mean-square error and estimated bias. The results of the simulation study suggested that both EM and EMB methods are applicable to the LFRM with EMB algorithm outperforms the standard EM algorithm. Illustration using a practical example and a real data set is provided.
In this study, we propose the estimation of the concentration parameter for simultaneous circular functional relationship model. In this case, the variances of the error term are not necessarily equal and the ratio of the concentration parameter λ = is not necessarily 1. The modified Bessel function was expended by using the asymptotic power series and it became a cubic equation of κ. From the cubic equation of κ, the roots were obtained by using the polyroot function in SPlus software. Simulation study was done to study the mean, estimated bias, absolute relative estimated bias, estimated standard error and estimated root mean square error of the estimation of the concentration parameter. From the simulation study, large concentration parameter and sample size show that the estimated concentration parameter has smaller bias. Also, an illustration to a real wind and wave data set is given to show its practical applicability.
Replicated linear functional relationship model is often used to describe
relationships between two circular variables where both variables have error terms and
replicate observations are available. We derive the estimate of the rotation parameter
of the model using the maximum likelihood method. The performance of the proposed
method is studied through simulation, and it is found that the biasness of the estimates
is small, thus implying the suitability of the method. Practical application of the
method is illustrated by using a real data set.
Cardiovascular disease (CVD) includes coronary heart disease, cerebrovascular disease (stroke), peripheral artery disease, and atherosclerosis of the aorta. All females face the threat of CVD. But becoming aware of symptoms and signs is a great challenge since most adults at increased risk of cardiovascular disease (CVD) have no symptoms or obvious signs especially in females. The symptoms may be identified by the assessment of their risk factors. The Bayesian approach is a specific way in dealing with this kind of problem by formalizing a priori beliefs and of combining them with the available observations. This study aimed to identify associated risk factors in CVD among female patients presenting with ST Elevation Myocardial Infarction (STEMI) using Bayesian logistic regression and obtain a feasible model to describe the data. A total of 874 STEMI female patients in the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the univariate and multivariate analysis. Model performance was assessed through the model calibration and discrimination. The final multivariate model of STEMI female patients consisted of six significant variables namely smoking, dyslipidaemia, myocardial infarction (MI), renal disease, Killip class and age group. Females aged 65 years and above have higher incidence of CVD and mortality is high among female patients with Killip class IV. Also, renal disease was a strong predictor of CVD mortality. Besides, performance measures for the model was considered good. Bayesian logistic regression model provided a better understanding on the associated risk factors of CVD for female patients which may help tailor prevention or treatment plans more effectively.
Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a
data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the
forecast a linear relationship with other factors but MLR has a disadvantage of having difficulties in modelling a nonlinear
relationship between the variables and influencing factors. Neural network (NN) model, on the other hand, is a good
model for modelling a nonlinear data. Therefore, in this study, a combination of MLR and NN models has proposed this
combination to overcome the problem. This hybrid model is then compared with MLR and NN models to see the performance
of the hybrid model. RMSE is used as a performance indicator and a proposed graphical error plot is introduce to see the
error graphically. From the result obtained this model gives a better forecast compare to the other two models.
Learning Strategy and Study Inventory (LASSI) merupakan suatu instrumen laporan kendiri yang digunakan untuk menilai strategi pembelajaran berdasarkan model umum pembelajaran kognitif dan model strategik pembelajaran. untuk mendapatkan maklumat tentang strategi pembelajaran pelajar Perubatan dan Pergigian di Institusi Pengajian Tinggi Awam (IPTA) dan Swasta (IPTS). Instrumen yang telah dibangunkan oleh LASSI diadaptasi dan digunapakai. Tiga komponen utama yang diukur dalam LASSI iaitu KEMAHuAn, KEMAHIrAn dan PErATurAn KEnDIrI. Populasi kajian merangkumi pelajar lepasan STPM dan Matrikulasi dari IPTA dan IPTS yang mengikuti program Perubatan serta Pergigian atau program Perubatan sahaja. Secara keseluruhannya, persepsi pelajar menunjukkan keperihatinan pelajar untuk mempelajari maklumat baru, sikap dan minat terhadap bidang yang dipelajari dan disiplin diri amat rendah berbanding pelajar di negara maju. Perbandingan skor pelajar IPTA dan IPTS menunjukkan terdapat perbezaan bagi faktor Kebimbangan, Pemprosesan Maklumat dan Strategi Pengujianan. Perbandingan skor pelajar lepasan Matrikulasi dan STPM pula menunjukkan tiada perbezaan signifikan bagi hampir semua skor bagi faktor LASSI kecuali Mat Bantu Pembelajaran dan Pengujian Kendiri. Perbandingan skor pelajar Perubatan dan Pergigian pula menunjukkan strategi pembelajaran bagi kedua dua kumpulan pelajar yang mengikuti bidang kritikal tersebut adalah sama dengan tiada sebarang perbezaan yang signifikan dalam sebarang faktor LASSI. Dapatan kajian juga menunjukkan bahawa keseluruhan pelajar yang mengikuti program kritikal mempunyai strategi pembelajaran yang kurang baik. Sehubungan itu pihak pengurusan perlu mengambil inisiatif untuk membantu pelajar dalam memperkemaskan strategi pembelajaran mereka. Strategi pembelajaran yang kurang efektif akan mengundang kesan sampingan yang tidak sihat seperti kemurungan atau stress di kalangan pelajar.