Displaying publications 41 - 60 of 310 in total

Abstract:
Sort:
  1. Abdul Ghapor Hussin, Norli Anida Abdullah, Ibrahim Mohamed
    This paper gives a comprehensive discussion on complex regression model by extending the idea of regression model to circular variables. Various aspect have been considered such as the biasness of parameters, error assumptions and model checking. The advantage of this approach is that it allows the use of usual technique available in ordinary linear regression for the regression of circular variables. The quality of the estimates and the feasibility of the approach were illustrated via simulation. The model was then applied to the wave direction data.
    Matched MeSH terms: Models, Statistical
  2. Shamiri A, Hamzah N, Pirmoradian A
    Sains Malaysiana, 2011;40:1179-1186.
    This paper focuses on measuring risk due to extreme events going beyond the multivariate normal distribution of joint returns. The concept of tail dependence has been found useful as a tool to describe dependence between extreme data in finance. Specifically, we adopted a multivariate Copula-EGARCH approach in order to investigate the presence of conditional dependence between international financial markets. In addition, we proposed a mixed Clayton-Gumbel copula with estimators for measuring both, the upper and lower tail dependence. The results showed significant dependence for Singapore and Malaysia as well as for Singapore and US, while the dependence for Malaysia and US was relatively weak
    Matched MeSH terms: Models, Statistical
  3. Akta? S
    Sains Malaysiana, 2016;45:1565-1572.
    In incomplete contingency tables, some cells may contain structural zeros. The quasi-independence model, which is a generalization of the independence model, is most commonly model used to analyze incomplete contingency tables. Goodness of fit tests of the quasi-independence model are usually based on Pearson chi square test statistic and likelihood ratio test statistic. In power divergence statistics family, the selection of power divergence parameter is of interest in multivariate discrete data. In this study, a simulation study is conducted to evaluate the performance of the power divergence statistics under quasi independence model for particular power divergence parameters in terms of power values.
    Matched MeSH terms: Models, Statistical
  4. Kamel NS, Sim KS
    Scanning, 2004 12 23;26(6):277-81.
    PMID: 15612204
    During the last three decades, several techniques have been proposed for signal-to-noise ratio (SNR) and noise variance estimation in images, with different degrees of success. Recently, a novel technique based on the statistical autoregressive model (AR) was developed and proposed as a solution to SNR estimation in scanning electron microscope (SEM) image. In this paper, the efficiency of the developed technique with different imaging systems is proven and presented as an optimum solution to image noise variance and SNR estimation problems. Simulation results are carried out with images like Lena, remote sensing, and SEM. The two image parameters, SNR and noise variance, are estimated using different techniques and are compared with the AR-based estimator.
    Matched MeSH terms: Models, Statistical
  5. Nur Hafiza, Z., Maskat, M.Y., Wan Aida, W.M., Osman, H.
    MyJurnal
    A study was carried out to optimize the deacidification process for noni (Morinda citrifolia L.) extract using packed column of calcium carbonate. The experiments were based on a 3-level factorial design to study the optimum process of deacidification for M. citrifolia extract. The M. citrifolia extract was treated with CaCO3 packed in different column diameter (20, 25 and 30 mm), height of calcium carbonate (0, 0.5 and 1 cm) and feed rate (10, 30 and 50 ml/min). Physico-chemical characteristics which include pH, titratable acidity, turbidity, total polyphenol content and total soluble solids were measured. Results showed that only pH, titratable acidity and turbidity could be well represented using statistical models. For pH, only the effect of height of CaCO3 was found to be significant. While for titratable acidity and turbidity, effects of diameter column and height of CaCO3 were significant. The optimum conditions for the deacidification of M. citrifolia extract was by using a column diameter of 30 mm, CaCO3 height of 1 cm, and a feed rate of 50 ml/min.
    Matched MeSH terms: Models, Statistical
  6. Lim HS, Rajab J, Al-Salihi A, Salih Z, MatJafri MZ
    Environ Sci Pollut Res Int, 2022 Feb;29(7):9755-9765.
    PMID: 34505243 DOI: 10.1007/s11356-021-16321-z
    Air surface temperature (AST) is a crucial importance element for many applications such as hydrology, agriculture, and climate change studies. The aim of this study is to develop regression equation for calculating AST and to analyze and investigate the effects of atmospheric parameters (O3, CH4, CO, H2Ovapor, and outgoing longwave radiation (OLR)) on the AST value in Iraq. Dataset retrieved from the Atmospheric Infrared Sounder (AIRS) at EOS Aqua Satellite, spanning the years of 2003 to 2016, and multiple linear regression were used to achieve the objectives of the study. For the study period, the five atmospheric parameters were highly correlated (R, 0.855-0.958) with predicted AST. Statistical analyses in terms of β showed that OLR (0.310 to 1.053) contributes significantly in enhancing AST values. Comparisons among selected five stations (Mosul, Kanaqin, Rutba, Baghdad, and Basra) for the year 2010 showed a close agreement between the predicted and observed AST from AIRS, with values ranging from 0.9 to 1.5 K and for ground stations data, within 0.9 to 2.6 K. To make more complete analysis, also, comparison between predicted and observed AST from AIRS for four selected month in 2016 (January, April, July, and October) has been carried out. The result showed a high correlation coefficient (R, 0.87 and 0.95) with less variability (RMSE ≤ 1.9) for all months studied, indicating model's capability and accuracy. In general, the results indicate the advantage of using the AIRS data and the regression analysis to investigate the impact of the atmospheric parameters on AST over the study area.
    Matched MeSH terms: Models, Statistical
  7. Wan Mohamad Nawi WIA, K Abdul Hamid AA, Lola MS, Zakaria S, Aruchunan E, Gobithaasan RU, et al.
    PLoS One, 2023;18(5):e0285407.
    PMID: 37172040 DOI: 10.1371/journal.pone.0285407
    Improving forecasting particularly time series forecasting accuracy, efficiency and precisely become crucial for the authorities to forecast, monitor, and prevent the COVID-19 cases so that its spread can be controlled more effectively. However, the results obtained from prediction models are inaccurate, imprecise as well as inefficient due to linear and non-linear patterns exist in the data set, respectively. Therefore, to produce more accurate and efficient COVID-19 prediction value that is closer to the true COVID-19 value, a hybrid approach has been implemented. Thus, aims of this study is (1) to propose a hybrid ARIMA-SVM model to produce better forecasting results. (2) to investigate in terms of the performance of the proposed models and percentage improvement against ARIMA and SVM models. statistical measurements such as MSE, RMSE, MAE, and MAPE then conducted to verify that the proposed models are better than ARIMA and SVM models. Empirical results with three real datasets of well-known cases of COVID-19 in Malaysia show that, compared to the ARIMA and SVM models, the proposed model generates the smallest MSE, RMSE, MAE and MAPE values for the training and testing datasets, means that the predicted value from the proposed model is closer to the actual value. These results prove that the proposed model can generate estimated values more accurately and efficiently. As compared to ARIMA and SVM, our proposed models perform much better in terms of error reduction percentages for all datasets. This is demonstrated by the maximum scores of 73.12%, 74.6%, 90.38%, and 68.99% in the MAE, MAPE, MSE, and RMSE, respectively. Therefore, the proposed model can be the best and effective way to improve prediction performance with a higher level of accuracy and efficiency in predicting cases of COVID-19.
    Matched MeSH terms: Models, Statistical
  8. Cheong YL, Leitão PJ, Lakes T
    Spat Spatiotemporal Epidemiol, 2014 Jul;10:75-84.
    PMID: 25113593 DOI: 10.1016/j.sste.2014.05.002
    The transmission of dengue disease is influenced by complex interactions among vector, host and virus. Land use such as water bodies or certain agricultural practices have been identified as likely risk factors for dengue because of the provision of suitable habitats for the vector. Many studies have focused on the land use factors of dengue vector abundance in small areas but have not yet studied the relationship between land use factors and dengue cases for large regions. This study aims to clarify if land use factors other than human settlements, e.g. different types of agricultural land use, water bodies and forest are associated with reported dengue cases from 2008 to 2010 in the state of Selangor, Malaysia. From the correlative relationship, we aim to generate a prediction risk map. We used Boosted Regression Trees (BRT) to account for nonlinearities and interactions between the factors with high predictive accuracies. Our model with a cross-validated performance score (Area Under the Receiver Operator Characteristic Curve, ROC AUC) of 0.81 showed that the most important land use factors are human settlements (model importance of 39.2%), followed by water bodies (16.1%), mixed horticulture (8.7%), open land (7.5%) and neglected grassland (6.7%). A risk map after 100 model runs with a cross-validated ROC AUC mean of 0.81 (±0.001 s.d.) is presented. Our findings may be an important asset for improving surveillance and control interventions for dengue.
    Matched MeSH terms: Models, Statistical*
  9. Khoshnevisan B, Rajaeifar MA, Clark S, Shamahirband S, Anuar NB, Mohd Shuib NL, et al.
    Sci Total Environ, 2014 May 15;481:242-51.
    PMID: 24602908 DOI: 10.1016/j.scitotenv.2014.02.052
    In this study the environmental impact of consolidated rice farms (CF) - farms which have been integrated to increase the mechanization index - and traditional farms (TF) - small farms with lower mechanization index - in Guilan Province, Iran, were evaluated and compared using Life cycle assessment (LCA) methodology and adaptive neuro-fuzzy inference system (ANFIS). Foreground data were collected from farmers using face-to-face questionnaires and background information about production process and inventory data was taken from the EcoInvent®2.0 database. The system boundary was confined to within the farm gate (cradle to farm gate) and two functional units (land and mass based) were chosen. The study also included a comparison of the input-output energy flows of the farms. The results revealed that the average amount of energy consumed by the CFs was 57 GJ compared to 74.2 GJ for the TFs. The energy ratios for CFs and TFs were 1.6 and 0.9, respectively. The LCA results indicated that CFs produced fewer environmental burdens per ton of produced rice. When compared according to the land-based FU the same results were obtained. This indicates that the differences between the two types of farms were not caused by a difference in their production level, but rather by improved management on the CFs. The analysis also showed that electricity accounted for the greatest share of the impact for both types of farms, followed by P-based and N-based chemical fertilizers. These findings suggest that the CFs had superior overall environmental performance compared to the TFs in the study area. The performance metrics of the model based on ANFIS show that it can be used to predict the environmental burdens of rice production with high accuracy and minimal error.
    Matched MeSH terms: Models, Statistical*
  10. Mirsalehy A, Abu Bakar MR, Lee LS, Jaafar AB, Heydar M
    ScientificWorldJournal, 2014;2014:138923.
    PMID: 24883350 DOI: 10.1155/2014/138923
    A novel technique has been introduced in this research which lends its basis to the Directional Slack-Based Measure for the inverse Data Envelopment Analysis. In practice, the current research endeavors to elucidate the inverse directional slack-based measure model within a new production possibility set. On one occasion, there is a modification imposed on the output (input) quantities of an efficient decision making unit. In detail, the efficient decision making unit in this method was omitted from the present production possibility set but substituted by the considered efficient decision making unit while its input and output quantities were subsequently modified. The efficiency score of the entire DMUs will be retained in this approach. Also, there would be an improvement in the efficiency score. The proposed approach was investigated in this study with reference to a resource allocation problem. It is possible to simultaneously consider any upsurges (declines) of certain outputs associated with the efficient decision making unit. The significance of the represented model is accentuated by presenting numerical examples.
    Matched MeSH terms: Models, Statistical*
  11. Pek CK, Jamal O
    J Environ Manage, 2011 Nov;92(11):2993-3001.
    PMID: 21820795 DOI: 10.1016/j.jenvman.2011.07.013
    In Malaysia, most municipal wastes currently are disposed into poorly managed 'controlled tipping' systems with little or no pollution protection measures. This study was undertaken to assist the relevant governmental bodies and service providers to identify an improved waste disposal management strategy. The study applied the choice experiment technique to estimate the nonmarket values for a number of waste disposal technologies. Implicit prices for environmental attributes such as psychological fear, land use, air pollution, and river water quality were estimated. Compensating surplus estimates incorporating distance from the residences of the respondents to the proposed disposal facility were calculated for a number of generic and technology-specific choice sets. The resulting estimates were higher for technology-specific options, and the distance factor was a significant determinant in setting an equitable solid waste management fee.
    Matched MeSH terms: Models, Statistical*
  12. Wibowo TC, Saad N
    ISA Trans, 2010 Jul;49(3):335-47.
    PMID: 20304404 DOI: 10.1016/j.isatra.2010.02.005
    This paper discusses the empirical modeling using system identification technique with a focus on an interacting series process. The study is carried out experimentally using a gaseous pilot plant as the process, in which the dynamic of such a plant exhibits the typical dynamic of an interacting series process. Three practical approaches are investigated and their performances are evaluated. The models developed are also examined in real-time implementation of a linear model predictive control. The selected model is able to reproduce the main dynamic characteristics of the plant in open-loop and produces zero steady-state errors in closed-loop control system. Several issues concerning the identification process and the construction of a MIMO state space model for a series interacting process are deliberated.
    Matched MeSH terms: Models, Statistical*
  13. Wai WW, Alkarkhi AF, Easa AM
    J Food Sci, 2009 Oct;74(8):C637-41.
    PMID: 19799660 DOI: 10.1111/j.1750-3841.2009.01331.x
    Response surface methodology (RSM) was carried out to study the effect of temperature, pH, and heating time as input variables on the yield and degree of esterification (DE) as the output (responses). The results showed that yield and DE of extracted pectin ranged from 2.27% to 9.35% (w/w, based on dry weight of durian rind) and 47.66% to 68.6%, respectively. The results also showed that a 2nd-order model adequately fitted the experimental data for the yield and DE. Optimum condition for maximum yield and DE was achieved at 85 degrees C, a time of either 4 or 1 h, and a pH of 2 or 2.5.
    Matched MeSH terms: Models, Statistical*
  14. Sumathi S, Bhatia S, Lee KT, Mohamed AR
    Bioresour Technol, 2009 Feb;100(4):1614-21.
    PMID: 18952414 DOI: 10.1016/j.biortech.2008.09.020
    Optimizing the production of microporous activated carbon from waste palm shell was done by applying experimental design methodology. The product, palm shell activated carbon was tested for removal of SO2 gas from flue gas. The activated carbon production was mathematically described as a function of parameters such as flow rate, activation time and activation temperature of carbonization. These parameters were modeled using response surface methodology. The experiments were carried out as a central composite design consisting of 32 experiments. Quadratic models were developed for surface area, total pore volume, and microporosity in term of micropore fraction. The models were used to obtain the optimum process condition for the production of microporous palm shell activated carbon useful for SO2 removal. The optimized palm shell activated carbon with surface area of 973 m(2)/g, total pore volume of 0.78 cc/g and micropore fraction of 70.5% showed an excellent agreement with the amount predicted by the statistical analysis. Palm shell activated carbon with higher surface area and microporosity fraction showed good adsorption affinity for SO2 removal.
    Matched MeSH terms: Models, Statistical*
  15. Wang M, Han L, Liu S, Zhao X, Yang J, Loh SK, et al.
    Biotechnol J, 2015 Sep;10(9):1424-33.
    PMID: 26121186 DOI: 10.1002/biot.201400723
    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed.
    Matched MeSH terms: Models, Statistical*
  16. Shafie AA, Vasan Thakumar A
    Eur J Health Econ, 2020 Dec;21(9):1411-1420.
    PMID: 32892230 DOI: 10.1007/s10198-020-01233-5
    OBJECTIVE: This study aimed to test multiplicative modelling with EQ-5D-3L time-trade-off (TTO) and visual analogue scale (VAS) values.

    METHODS: A multi-stage sampling design was adopted for the study and data collection took place in three phases in 2010, 2011, and 2012 in the Northern region of Malaysia. Face-to-face interviews involved respondents answering both 13 TTO and 15 VAS valuation tasks were carried out. Both additive and multiplicative model specifications were explored using the valuation data. Model performance was evaluated using out-of-sample predictive accuracy by applying the cross-validation technique. The distribution of the model values was also graphically compared on Bland-Altman plots and kernel density distribution curves.

    RESULTS: Data from 630 and 611 respondents were included for analyses using TTO and VAS models, respectively. In terms of main-effects specifications, cross-validation results revealed a slight superiority of multiplicative models over its additive counterpart in modelling TTO values. However, both main-effects models had roughly equal predictive accuracy for VAS models. The non-linear multiplicative model with I32 term, MULT7_TTO, performed best for TTO models; while, the linear additive model with N3 term, ADD11_VAS, outperformed the other VAS models. Multiplicative modelling neither altered the dimensional rankings of importance nor did it change the distribution of values of the health states.

    CONCLUSION: Using EQ-5D-3L valuation data, multiplicative modelling was shown to improve out-of-sample predictive accuracy of TTO models but not of VAS models.

    Matched MeSH terms: Models, Statistical*
  17. Sheikh Ghadzi SM, Karlsson MO, Kjellsson MC
    CPT Pharmacometrics Syst Pharmacol, 2017 10;6(10):686-694.
    PMID: 28575547 DOI: 10.1002/psp4.12214
    In antihyperglycemic drug development, drug effects are usually characterized using glucose provocations. Analyzing provocation data using pharmacometrics has shown powerful, enabling small studies. In preclinical drug development, high power is attractive due to the experiment sizes; however, insulin is not always available, which potentially impacts power and predictive performance. This simulation study was performed to investigate the implications of performing model-based drug characterization without insulin. The integrated glucose-insulin model was used to simulate and re-estimated oral glucose tolerance tests using a crossover design of placebo and study compound. Drug effects were implemented on seven different mechanisms of action (MOA); one by one or in two-drug combinations. This study showed that exclusion of insulin may severely reduce the power to distinguish the correct from competing drug effect, and to detect a primary or secondary drug effect, however, it did not affect the predictive performance of the model.
    Matched MeSH terms: Models, Statistical*
  18. Bonakdari H, Ebtehaj I, Akhbari A
    Water Sci Technol, 2017 Jun;75(12):2791-2799.
    PMID: 28659519 DOI: 10.2166/wst.2017.158
    Electrocoagulation (EC) is employed to investigate the energy consumption (EnC) of synthetic wastewater. In order to find the best process conditions, the influence of various parameters including initial pH, initial dye concentration, applied voltage, initial electrolyte concentration, and treatment time are investigated in this study. EnC is considered the main criterion of process evaluation in investigating the effect of the independent variables on the EC process and determining the optimum condition. Evolutionary polynomial regression is combined with a multi-objective genetic algorithm (EPR-MOGA) to present a new, simple and accurate equation for estimating EnC to overcome existing method weaknesses. To survey the influence of the effective variables, six different input combinations are considered. According to the results, EPR-MOGA Model 1 is the most accurate compared to other models, as it has the lowest error indices in predicting EnC (MARE = 0.35, RMSE = 2.33, SI = 0.23 and R2 = 0.98). A comparison of EPR-MOGA with reduced quadratic multiple regression methods in terms of feasibility confirms that EPR-MOGA is an effective alternative method. Moreover, the partial derivative sensitivity analysis method is employed to analyze the EnC variation trend according to input variables.
    Matched MeSH terms: Models, Statistical*
  19. Simoneau G, Levis B, Cuijpers P, Ioannidis JPA, Patten SB, Shrier I, et al.
    Biom J, 2017 Nov;59(6):1317-1338.
    PMID: 28692782 DOI: 10.1002/bimj.201600184
    Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings.
    Matched MeSH terms: Models, Statistical*
  20. Zare H, Tavana M, Mardani A, Masoudian S, Kamali Saraji M
    Health Care Manag Sci, 2019 Sep;22(3):475-488.
    PMID: 30225622 DOI: 10.1007/s10729-018-9456-4
    Performance measurement plays an important role in the successful design and reform of regional healthcare management systems. In this study, we propose a hybrid data envelopment analysis (DEA) and game theory model for measuring the performance and productivity in the healthcare centers. The input and output variables associated with the efficiency of the healthcare centers are identified by reviewing the relevant literature, and then used in conjunction with the internal organizational data. The selected indicators and collected data are then weighted and prioritized with the help of experts in the field. A case study is presented to demonstrate the applicability and efficacy of the proposed model. The results reveal useful information and insights on the efficiency levels of the regional healthcare centers in the case study.
    Matched MeSH terms: Models, Statistical*
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links