• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.247-263
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• 2001

This paper concerns a problem of classification based on training dta. A framework of information geometry is given to elucidate the characteristics of discriminant functions including logistic discrimination and AdaBoost. We discuss a class of loss functions from a unified viewpoint.

• The Korean Journal of Applied Statistics
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• v.1 no.2
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• pp.45-60
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• 1987

Deciding whether a certain cancer patient is suffering from a bone marrow metastasis is quite essential to clinicians. To find a set of explanatory variables of the bone marrow metastasis, we employed the logistic regression analysis on 60 cancer patients with bone marrow metastasis (the case group) and 41 cancer patients without bone marrow metastasis (the control group). These data shown in Append were collected retrospectively from the record of Severance Hospital of Yonsei University College of Medicine from January, 1977 to December, 1985. We could establish a set of decision rules of the bone marrow metastasis specially designed for clinicians based on the explanatory variables of the best fitting logistic regression equation. We also compute the specifity and the sensistivity of our decision rules.

• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.453-464
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• 2018

Purpose: The purpose of this study is to propose a solution to the case where $V_{50}$ calculation is impossible in the process of bulletproof test. Methods: In this study, we proposed a $V_{50}$ estimation method using logistic regression analysis. Six scenarios were applied by combining the homogeneity of the sample and the speed range. Then, 1,000 simulations were performed per scenario and six assumptions reflecting the reality were applied. Results: The result of the study, it was confirmed that there was no statistical difference between the $V_{50}$ value calculated by the conventional method and the $V_{50}$ value calculated by the improvement method. Therefore, in situations where $V_{50}$ can not be calculated, it is reasonable to use logistic regression analysis. Conclusion: This study develops a methodology that is easy to use and reliable by using statistical model based on actual data.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.454-464
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• 2010

A probability prediction model for tropical cyclone (TC) genesis in the Northwestern Pacific area was developed using the logistic regression method. Total five predictors were used in this model: the lower-level relative vorticity, vertical wind shear, mid-level relative humidity, upper-level equivalent potential temperature, and sea surface temperature (SST). The values for four predictors except for SST were obtained from difference of spatial-averaged value between May and January, and the time average of Ni$\tilde{n}$o-3.4 index from February to April was used to see the SST effect. As a result of prediction for the TC genesis frequency from June to December during 1951 to 2007, the model was capable of predicting that 21 (22) years had higher (lower) frequency than the normal year. The analysis of real data indicated that the number of year with the higher (lower) frequency of TC genesis was 28 (29). The overall predictability was about 75%, and the model reliability was also verified statistically through the cross validation analysis method.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.931-940
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• 2011

Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and PCA based discriminant analysis by some real and artificial data. It is concluded that the new method has better power as compared with other methods.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.653-669
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• 2019

The development of new media has gradually decreased the use of newspapers, which had previously occupied the largest share of media. Subscriptions have declined gradually and fell to 14 percent in 2016. This study explores the effects of Newspaper reader's characteristics on regular newspaper subscriptions. The data used for analysis was provided by the Korean Press Foundation and Media Audience Awareness Survey Data in 2016 and 2017. We considered gender, age, education, income, number of days of reading, reading time and amount of reading as the characteristics of the reader. Multiple logistic regression was fitted and interpreted to see what characteristics affect regular subscription.

• Journal of the Korea Society for Simulation
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• v.27 no.1
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• pp.51-60
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• 2018

This study aims to understand crucial factors affecting user's Fintech payment service adoption. On the basis of innovation diffusion theory and prior Fintech literature, this study classifies the influence factors of users' adoption of Fintech payment service into two dimensions - service dimension containing complexity, perceived benefit, trust in service provider and user dimension containing personal innovativeness and security breach experience. The data analysis results using binary logistic regression shows the negative direct effects of perceived risk, complexity, security accident experience on user's service adoption are statistically significant. Personal innovativeness has a positive effect on user's Fintech payment service adoption. The moderation effect of security accident experience is also significant at p<0.05.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.603-624
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• 2020

There is an emerging interest in brain functional connectivity (FC) based on functional Magnetic Resonance Imaging in Alzheimer's disease (AD) studies. The complex and high-dimensional structure of FC makes it challenging to explore the association between altered connectivity and AD susceptibility. We develop a pipeline to refine FC as proper covariates in a penalized logistic regression model and classify normal and AD susceptible groups. Three different quantification methods are proposed for FC refinement. One of the methods is dimension reduction based on common component analysis (CCA), which is employed to address the limitations of the other methods. We applied the proposed pipeline to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data and deduced pathogenic FC biomarkers associated with AD susceptibility. The refined FC biomarkers were related to brain regions for cognition, stimuli processing, and sensorimotor skills. We also demonstrated that a model using CCA performed better than others in terms of classification performance and goodness-of-fit.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.15-20
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• 2021

This paper was conducted to prevent and respond to crimes by predicting crimes based on artificial intelligence. While the quality of life is improving with the recent development of science and technology, various problems such as poverty, unemployment, and crime occur. Among them, in the case of crime problems, the importance of crime prediction increases as they become more intelligent, advanced, and diversified. For all crimes, it is more critical to predict and prevent crimes in advance than to deal with them well after they occur. Therefore, in this paper, we predicted crime types and crime tools using the Multiclass Logistic Regression algorithm and Multiclass Neural Network algorithm of machine learning. Multiclass Logistic Regression algorithm showed higher accuracy, precision, and recall for analysis and prediction than Multiclass Neural Network algorithm. Through these analysis results, it is expected to contribute to a more pleasant and safe life by implementing a crime prediction system that predicts and prevents various crimes. Through further research, this researcher plans to create a model that predicts the probability of a criminal committing a crime again according to the type of offense and deploy it to a web service.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.339-350
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• 2021

In medical research, the risk factors associated with human diseases need to be identified to predict the incidence rate and determine the treatment plan. Logistic regression analysis is primarily used in order to select risk factors. However, individuals who are unfamiliar with statistics outcomes have trouble using these methods. In this study, we develop a nomogram that graphically represents the numerical association between the disease and risk factors in order to identify the risk factors for delirium and to interpret and use the results more effectively. By using the logistic regression model, we identify risk factors related to delirium, construct a nomogram and predict incidence rates. Additionally, we verify the developed nomogram using a receiver operation characteristics (ROC) curve and calibration plot. Nursing home, stroke/epilepsy, metabolic abnormality, hemodynamic instability, and analgesics were selected as risk factors. The validation results of the nomogram, built with the factors of training set and the test set of the AUC showed a statistically significant determination of 0.893 and 0.717, respectively. As a result of drawing the calibration plot, the coefficient of determination was 0.820. By using the nomogram developed in this paper, health professionals can easily predict the incidence rate of delirium for individual patients. Based on this information, the nomogram could be used as a useful tool to establish an individual's treatment plan.