• Communications for Statistical Applications and Methods
• /
• v.28 no.5
• /
• pp.425-445
• /
• 2021

A generalization of the log-logistic (LL) distribution called exponentiated log-logistic (ELL) distribution on lines of exponentiated Weibull distribution is considered. In this paper, based on progressive type-II censored samples, we have derived the maximum likelihood estimators and Bayes estimators for three parameters, the survival function and hazard function of the ELL distribution. Then, under the balanced squared error loss (BSEL) and the balanced linex loss (BLEL) functions, their corresponding Bayes estimators are obtained using Lindley's approximation (see Jung and Chung, 2018; Lindley, 1980), Tierney-Kadane approximation (see Tierney and Kadane, 1986) and Markov Chain Monte Carlo methods (see Hastings, 1970; Gelfand and Smith, 1990). Here, to check the convergence of MCMC chains, the Gelman and Rubin diagnostic (see Gelman and Rubin, 1992; Brooks and Gelman, 1997) was used. On the basis of their risks, the performances of their Bayes estimators are compared with maximum likelihood estimators in the simulation studies. In this paper, research supports the conclusion that ELL distribution is an efficient distribution to modeling data in the analysis of survival data. On top of that, Bayes estimators under various loss functions are useful for many estimation problems.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.2
• /
• pp.124-132
• /
• 2023

In this paper, we present a simple classification model based on statistical features and demonstrate the successful implementation of a brain-computer interface (BCI) based light on/off control system. This research shows study and development of light on/off control system based on BCI technology, which allows the users to control switching a lamp using electroencephalogram (EEG) signals. The logistic regression algorithm is used for classification of the EEG signal to convert it into light on, light off control commands. Training data were collected using 14-channel BCI system which records the brain signals of participants watching a screen with flickering lights and saves the data into .csv file for future analysis. After extracting a number of features from the data and performing classification using logistic regression, we created commands to switch on a physical lamp and tested it in a real environment. Logistic regression allowed us to quite accurately classify the EEG signals based on the user's mental state and we were able to classify the EEG signals with 82.5% accuracy, producing reliable commands for turning on and off the light.

• Communications for Statistical Applications and Methods
• /
• v.24 no.3
• /
• pp.271-290
• /
• 2017

We study a bivariate response regression model with arbitrary marginal distributions and joint distributions using Frank and Clayton's families of copulas. The proposed model is used for fitting dependent bivariate data with explanatory variables using the log-odd log-logistic Weibull distribution. We consider likelihood inferential procedures based on constrained parameters. For different parameter settings and sample sizes, various simulation studies are performed and compared to the performance of the bivariate odd-log-logistic-Weibull regression model. Sensitivity analysis methods (such as local and total influence) are investigated under three perturbation schemes. The methodology is illustrated in a study to assess changes on schoolchildren's oral health-related quality of life (OHRQoL) in a follow-up exam after three years and to evaluate the impact of caries incidence on the OHRQoL of adolescents.

• Communications for Statistical Applications and Methods
• /
• v.26 no.6
• /
• pp.557-573
• /
• 2019

Mortality study is an essential component of actuarial risk management for life insurance policies, annuities, and pension plans. Life expectancy has drastically increased over the last several decades; consequently, longevity risk associated with annuity products and pension systems has emerged as a crucial issue. Among the various aspects of mortality study, a consideration of the cause-of-death mortality can provide a more comprehensive understanding of the nature of mortality/longevity risk. In this case study, the cause-of-mortality data in Korea and the US were analyzed along with a multinomial logistic regression model that was constructed to quantify the impact of mortality reduction in a specific cause on actuarial values. The results of analyses imply that mortality improvement due to a specific cause should be carefully monitored and reflected in mortality/longevity risk management. It was also confirmed that multinomial logistic regression model is a useful tool for analyzing cause-of-death mortality for actuarial applications.

• Industrial Engineering and Management Systems
• /
• v.13 no.4
• /
• pp.449-453
• /
• 2014

Chronic diseases including hypertension and its complications are major sources causing the national medical expenditures to increase. We aim to predict the risk of hypertension complications for hypertension patients, using the sample national healthcare database established by Korean National Health Insurance Corporation. We apply classification techniques, such as logistic regression, linear discriminant analysis, and classification and regression tree to predict the hypertension complication onset event for each patient. The performance of these three methods is compared in terms of accuracy, sensitivity and specificity. The result shows that these methods seem to perform similarly although the logistic regression performs marginally better than the others.

• Journal of the Korean Society of Safety
• /
• v.25 no.4
• /
• pp.90-95
• /
• 2010

This study deals with the accident model of arterial link section in Cheongju. The objective is to develop the accident model of arterial link section using the logistic regression. In pursuing the above, the study uses the 258 accident data occurred at the 322 arterial link section. The main results are as follows. First, Nagellerke $R^2$ of developed accident model is analyzed to be 0.309 and t-values of variable that explains goodness of fit are evaluated to be significant. Second, the variables adopted in the model are AADT, the number of exit and entry. These variables are all analyzed to be statistically significant. Finally, the analysis of correct classification rate shows that the total accident of correct classification rate is analyzed to be 72.7% at the arterial link section.

• Communications for Statistical Applications and Methods
• /
• v.8 no.3
• /
• pp.581-588
• /
• 2001

The $ED_{100p}$ is that value of the dose associated with 100p% response rate in the analysis of quantal response data. Brand, Pinnock, and Jackson (1973) studied the confidence bands of $ED_{100p}$ obtained by solving extremal values algebraically on the ellipsoid confidence region of the parameters in the simple logistic regression model. In this paper, we develope and illustrate a simpler method for obtaining confidence bands for $ED_{100p}$ based on the rectangular confidence region of parameters.

• The Journal of Information Systems
• /
• v.18 no.3
• /
• pp.375-399
• /
• 2009

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.12
• /
• pp.5963-5980
• /
• 2017

Image scrambling is an important technology in information hiding, where the Arnold transformation is widely used. Several researchers have proposed the application of $H{\acute{e}}non$ map in square image scrambling, and certain improved technologies require scrambling many times to achieve a good effect without resisting chosen-plaintext attack although it can be directly applied to non-square images. This paper presents a non-square image scrambling algorithm, which can resist chosen-plaintext attack based on a chaotic two-dimensional Sine Logistic modulation map and $H{\acute{e}}non$ map (2D-SLHM). Theoretical analysis and experimental results show that the proposed algorithm has advantages in terms of key space, efficiency, scrambling degree, ability of anti-attack and robustness to noise interference.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2017.05a
• /
• pp.264-265
• /
• 2017

This paper aims to analyze relations between design errors prevented by building information modeling (BIM)-based design validation and their impacts in order to identify critical consideration factors for successfully implementing BIM-based design validation in the architecture, engineering, and construction (AEC) projects. More than 800 design errors detected by BIM-based design validation in two BIM-based projects in South Korea are categorized according to its causes and work types. The relations between causes and work types of design errors and project delay, cost overrun, low quality, and rework generation that can be caused by the errors are analyzed through conducting logistic regression. Characteristics of each design error are analyzed by conducting face-to-face interviews with practitioners in the two BIM-based projects. As the results, the impacts of design error causes on predicting project delay, cost overrun, low quality, and rework generation were the highest.