• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.385-396
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• 2018

Competing risks are commonly encountered in biomedical research. Regression models for competing risks data can be developed based on data routinely collected in hospitals or general practices. However, these data sets usually contain the covariate missing values. To overcome this problem, multiple imputation is often used to fit regression models under a MAR assumption. Here, we introduce a multivariate imputation in a chained equations algorithm to deal with competing risks survival data. Using pseudo-observations, we make use of the available outcome information by accommodating the competing risk structure. Lastly, we illustrate the practical advantages of our approach using simulations and two data examples from a coronary artery disease data and hepatocellular carcinoma data.

• Journal of Information Technology Applications and Management
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• v.11 no.1
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• pp.161-174
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• 2004

This study is intended to suggest1 the optimized data mining model for the efficient customer credit evaluation in the capital finance industry. To accomplish the research objective, various data mining models for the customer credit evaluation are compared and analyzed. Furthermore, existing models such as Multi-Layered Perceptrons, Multivariate Discrimination Analysis, Radial Basis Function, Decision Tree, and Logistic Regression are employed for analyzing the customer information in the capital finance market and the detailed data of capital financing transactions. Finally, the data from the integrated model utilizing a genetic algorithm is compared with those of each individual model mentioned above. The results reveals that the integrated model is superior to other existing models.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1021-1030
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• 2018

Objective: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. Materials and Methods: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (${\Delta}HUTEST$) and CCTA (${\Delta}HUCCTA$). We developed GLMs to predict ${\Delta}HUCCTA$. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis. Results: In multivariate analysis, only total body weight (TBW) and ${\Delta}HUTEST$ maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ${\Delta}HUCCTA$ and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, $-0.0{\pm}5.1$ Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ${\Delta}HUCCTA$ ($-0.0{\pm}5.9HU/gI$; 95% CI, -11.9, 11.9) and TBW ($1.1{\pm}6.2HU/gI$; 95% CI, -11.2, 13.4). Conclusion: We demonstrated that the patient's TBW and ${\Delta}HUTEST$ significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.

• Computers and Concrete
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• v.19 no.5
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• pp.467-475
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• 2017

The purpose of this study is to establish a prediction model for the electrical resistivity ($E_r$) of self-consolidating concrete by using waste LCD (liquid crystal display) glass as part of the fine aggregate and then, to analyze the results obtained from a series of laboratory tests. A hyperbolic function is used to perform nonlinear multivariate regression analysis of the electrical resistivity prediction model, with parameters such as water-binder ratio (w/b), curing age (t) and waste glass content (G). Furthermore, the relationship of compressive strength and electrical resistivity of waste LCD glass concrete is also found by a logarithm function, while compressive strength is evaluated by the electrical resistivity of non-destructive testing (NDT). According to relative regression analysis, the electrical resistivity and compressive strength prediction models are developed, and the results show that a good agreement is obtained using the proposed prediction models. From the comparison between the predicted analysis values and test results, the MAPE value of electrical resistivity is 17.0-18.2% and less than 20%, the MAPE value of compressive strength evaluated by $E_r$ is 5.9-10.6% and nearly less than 10%. Therefore, the prediction models established in this study have good predictive ability for electrical resistivity and compressive strength of waste LCD glass concrete. However, further study is needed in regard to applying the proposed prediction models to other ranges of mixture parameters.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.797-803
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• 2001

This paper obtained Bayes prediction density for the spatial linear model with non-informative prior. It showed the results that predictive inferences is completely unaffected by departures from the normality assumption in the direction of the elliptical family and the structure of prediction density is unchanged by more than one additional future observations.

• Information Systems Review
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• v.8 no.3
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• pp.125-134
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• 2006

This article adopts a decision tree algorithm(C5.0) to predict customer churning in online auto insurance environment. Using a sample of on-line auto insurance customers contracts sold between 2003 and 2004, we test how decision tree-based model(C5.0) works on the prediction of customer churning. We compare the result of C5.0 with those of logistic regression model(LRM), multivariate discriminant analysis(MDA) model. The result shows C5.0 outperforms other models in the predictability. Based on the result, this study suggests a way of setting marketing strategy and of developing online auto insurance business.

• Geomechanics and Engineering
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• v.16 no.6
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• pp.577-588
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• 2018

A major concern in deep excavation project in soft clay deposits is the potential for adjacent buildings to be damaged as a result of the associated excessive ground movements. In order to accurately determine the wall deflections using a numerical procedure such as the finite element method, it is critical to use the correct soil parameters such as the stiffness/strength properties. This can be carried out by performing an inverse analysis using the measured wall deflections. This paper firstly presents the results of extensive plane strain finite element analyses of braced diaphragm walls to examine the influence of various parameters such as the excavation geometry, soil properties and wall stiffness on the wall deflections. Based on these results, a multivariate adaptive regression splines (MARS) model was developed for inverse parameter identification of the soil relative stiffness ratio. A second MARS model was also developed for inverse parameter estimation of the wall system stiffness, to enable designers to determine the appropriate wall size during the preliminary design phase. Soil relative stiffness ratios and system stiffness values derived via these two different MARS models were found to compare favourably with a number of field and published records.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.197-211
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• 2024

This research studies the effect of geotechnical factors on EPB-TBM performance parameters. The modeling was performed using simple and multivariate linear regression methods, artificial neural networks (ANNs), and Sugeno fuzzy logic (SFL) algorithm. In ANN, 80% of the data were randomly allocated to training and 20% to network testing. Meanwhile, in the SFL algorithm, 75% of the data were used for training and 25% for testing. The coefficient of determination (R²) obtained between the observed and estimated values in this model for the thrust force and cutterhead torque was 0.19 and 0.52, respectively. The results showed that the SFL outperformed the other models in predicting the target parameters. In this method, the R² obtained between observed and predicted values for thrust force and cutterhead torque is 0.73 and 0.63, respectively. The sensitivity analysis results show that the internal friction angle (φ) and standard penetration number (SPT) have the greatest impact on thrust force. Also, earth pressure and overburden thickness have the highest effect on cutterhead torque.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.596-600
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• 2009

To support an efficient management of software verification and validation activities, many defect prediction models have been proposed based on object oriented metrics. They usually adopt logistic regression analysis, And, they state that the correctness of prediction is about 60${\sim}$70%, We performed a similar experiment with Eclipse 3.3 to check their prediction effectiveness, However, the result shows that correctness is about 40% which is much lower than the original results. We also found that univariate logistic regression analysis produces better results than multivariate logistic regression analysis.