• Communications for Statistical Applications and Methods
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• 제20권2호
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• pp.129-136
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• 2013

Linear regression is the most basic statistical model for exploring the relationship between a numerical response variable and several explanatory variables. Logistic regression secures the role of linear regression for the dichotomous response variable. In this paper, we propose a biplot-type display of the multivariate data guided by the linear regression and/or the logistic regression. The figures show the directional flow of the response variable as well as the interrelationship of explanatory variables.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.250-255
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• 2007

Steam generator tubes play an important role in safety because they constitute one of the primary barriers between the radioactive and non-radioactive sides of the nuclear power plant. For this reason, the integrity of the tubes is essential in minimizing the leakage possibility of radioactive water. The integrity of the tubes is evaluated based on NDE (non-destructive evaluation) inspection results. Especially ECT (eddy current test) method is usually used for detecting the flaws in steam generator tubes. However, detection capacity of the NDE is not perfect and all of the "real flaws" which actually existing in steam generator tunes is not known by NDE results. Therefore reliability of NDE system is one of the essential parts in assessing the integrity of steam generators. In this study POD (probability of detection) of ECT system for ODSCC in steam generator tubes is evaluated using multivariate logistic regression. The cracked tube specimens are made using the withdrawn steam generator tubes. Therefore the cracks are not artificial but real. Using the multivariate logistic regression method, continuous POD surfaces are evaluated from hit (detection) and miss (no detection) binary data obtained from destructive and non-destructive evaluation of the cracked tubes. Length and depth of cracks are considered in multivariate logistic regression and their effects on detection capacity are evaluated.

• Geomechanics and Engineering
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• 제10권3호
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• pp.269-284
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• 2016

Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods were developed by analyzing liquefaction case histories from which the liquefaction boundary (limit state) separating two categories (the occurrence or non-occurrence of liquefaction) is determined. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model using conventional modeling techniques that take into consideration all the independent variables, such as the seismic and soil properties. In this study, a modification of the Multivariate Adaptive Regression Splines (MARS) approach based on Logistic Regression (LR) LR_MARS is used to evaluate seismic liquefaction potential based on actual field records. Three different LR_MARS models were used to analyze three different field liquefaction databases and the results are compared with the neural network approaches. The developed spline functions and the limit state functions obtained reveal that the LR_MARS models can capture and describe the intrinsic, complex relationship between seismic parameters, soil parameters, and the liquefaction potential without having to make any assumptions about the underlying relationship between the various variables. Considering its computational efficiency, simplicity of interpretation, predictive accuracy, its data-driven and adaptive nature and its ability to map the interaction between variables, the use of LR_MARS model in assessing seismic liquefaction potential is promising.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.447-456
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• 2007

Recently, the multivariate statistical analysis has been used to analyze meaningful information for various data. In this paper, we develope the multivariate statistical analysis system combined with Fisher discriminant analysis, logistic regression, neural network, and decision tree using visual basic 6.0.

• Journal of Korea Artificial Intelligence Association
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• 제1권2호
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• pp.7-13
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• 2023

In this paper, Depression is a mental disorder characterized by a lack of enthusiasm and feelings of sadness, which significantly impairs daily functioning. In 2018, there was an increase in book sales in the essay genre, particularly the popularity of "healing essays." This trend is seen as challenging the negative image and prejudices associated with depression. In 2021, a significant rise in the proportion of 20-year-old patients with depression is attributed to factors like job-related stress, interpersonal issues, and financial burdens. Additionally, there is a strong correlation between depression and suicidal thoughts, particularly among individuals who have experienced feelings of depression. Despite the increasing prevalence of depression among young adults, research in this area is lacking. To address this gap, statistical tools such as logistic regression and chi-squared tests are employed. The analysis reveals various independent variables associated with feelings of depression, shedding light on the relationships between these factors.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• 제27권2호
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• pp.113-124
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• 2024

Purpose: Alarm symptoms (red flag signs) are crucial indications for management decisions on pediatric gastrointestinal endoscopy. We aimed to identify items in the alarm symptoms and pre-endoscopic investigations that predict abnormal endoscopy results. Methods: A retrospective descriptive study was conducted among children aged under 18 years undergoing endoscopy. The patients were classified into normal and abnormal endoscopic groups. The incidence of alarm symptoms and pre-endoscopic investigations were compared between the groups. Univariate and multivariate logistic regression analyses were performed to determine independent risk factors for abnormal endoscopy. Results: Of 148 participants, 66 were classified in the abnormal endoscopy group. Compared with the normal group, the abnormal group had a significantly higher prevalence of alarm symptoms. Moreover, hematemesis/hematochezia, anemia, low hemoglobin level, hypoalbuminemia, rising erythrocyte sedimentation rate, increased serum lipase, and blood urea nitrogen/creatinine ratio were significantly higher in the abnormal endoscopy group than in the normal group. Multivariate logistic regression analysis indicated that hematemesis/hematochezia and low hemoglobin level were independent risk factors for abnormal endoscopy. Conclusion: The alarm symptoms and pre-endoscopic investigations were evaluated using predictive factors for abnormal pediatric endoscopic findings. According to multivariate logistic regression analysis, hematemesis/hematochezia and low hemoglobin levels were independent risk factors for abnormal endoscopy.

• 보건행정학회지
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• 제10권2호
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• pp.1-21
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• 2000

This study reviewde various parametic and nonparametic method for forexasting hospital closures in Korea. We compared multivariate discriminant analysis, multivartiate logistic regression, classfication and regression tree, and neural network method based on hit ratio of each model for forecasting hospital closure. Like other studies in the literture, neural metwork analysis showed highest average hit ratio. For policy and business purposes, we combined the four analytical method and constructed a foreasting model that can be easily used to predict the probabolity of hospital closure given financial information of a hospital.

• 대한산업공학회지
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• 제39권5호
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• pp.393-402
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• 2013

The Mahalanobis-Taguchi System (MTS) is a diagnostic and predictive method for multivariate data. In the MTS, the Mahalanobis space (MS) of reference group is obtained using the standardized variables of normal data. The Mahalanobis space can be used for multi-class classification. Once this MS is established, the useful set of variables is identified to assist in the model analysis or diagnosis using orthogonal arrays and signal-to-noise ratios. And other several techniques have already been used for classification, such as linear discriminant analysis and logistic regression, decision trees, neural networks, etc. The goal of this case study is to compare the ability of the Mahalanobis-Taguchi System and logistic regression using a data set.

• Journal of the Korean Data and Information Science Society
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• 제22권1호
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• pp.107-113
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• 2011

We present methods for studying the log-density ratio, which allow us to select which predictors are needed, and how they should be included in the logistic regression model. Under multivariate normal distributional assumptions, we investigate the form of the log-density ratio as a function of many predictors. The linear, quadratic and crossproduct terms are required in general. If two covariance matrices are equal, then the crossproduct and quadratic terms are not needed. If the variables are uncorrelated, we do not need the crossproduct terms, but we still need the linear and quadratic terms.

• Asia pacific journal of information systems
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• 제15권2호
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• pp.51-63
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• 2005

There has been substantial research into the bankruptcy prediction. Many researchers used the statistical method in the problem until the early 1980s. Since the late 1980s, Artificial Intelligence(AI) has been employed in bankruptcy prediction. And many studies have shown that artificial neural network(ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance, it has some problems such as overfitting and poor explanatory power. To overcome these limitations, this paper suggests a relatively new machine learning technique, support vector machine(SVM), to bankruptcy prediction. SVM is simple enough to be analyzed mathematically, and leads to high performances in practical applications. The objective of this paper is to examine the feasibility of SVM in bankruptcy prediction by comparing it with ANN, logistic regression, and multivariate discriminant analysis. The experimental results show that SVM provides a promising alternative to bankruptcy prediction.