• The Korean Journal of Applied Statistics
• /
• v.14 no.2
• /
• pp.449-462
• /
• 2001

베이지안 다중 검정방법(multiple hypothesis test)은 여러 통계모형에서 성공적인 결과를 주는 것으로 알려져있다. 일반적으로, 베이지안 가설검정은 고려중인 모형에 대한 사후확률을 계산하여 가장 높은 확률은 갖는 모형을 선택하기 때문에 귀무가설의 기각여부에만 관심을 가지는 고전적인 분산분석 검정과는 달리 좀 더 구체적인 모형을 선택할 수 있는 장점이 있다. 이 논문에서는 독립이면서 로그정규분포를 따르는 K($\geq$3)개 모집단의 모수에 대한 가설 검정방법으로 O’Hagan(1995)이 제안한 부분 베이즈 요인을 이용한 베이지안 방법을 제안한다. 이 때 모수에 대한 사전분포로는 무정보적 사전분포를 사용한다. 제안한 검정 방법의 유용성을 알아보기 위하여 실제 자료의 분석과 모의 실험을 이용하여 고전적인 검정방법과 그 결과를 비교한다.

• The Korean Journal of Applied Statistics
• /
• v.13 no.2
• /
• pp.427-436
• /
• 2000

In this paper, we consider a Bayesian model selection problem of lifetime distributions using fractional Bayes factor with noninformative prior when type II censored data are given. For a given type II censored data, we calculate the posterior probability of exponential, Weibull and lognormal distributions and select the model which gives the highest posterior probability. Our proposed methodology is explained and applied to real data and simulated data.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.601-613
• /
• 2005

일반적으로 B2C가 불특정 다수에 대한 서비스라면 B2B는 특정 소수에 대한 서비스라고 할 수 있다. 이러한 특성으로 B2C와 B2B에서 고객의 구매의도는 다르게 평가되어야 한다. 또한 B2B는 협상이라는 단계가 있고, 이것은 B2C와 B2B의 구매의도 평가기준에 영향을 미치게 된다. 본 연구에서는 B2B에서 구매의도 파악을 위한 웹 로그 분석 모형을 제시한다. 제시된 모형을 통해 구매의도 파악을 위한 웹 로그 분석 데이터를 추출하고, 추출된 데이터를 기업의 레거시 시스템 데이터와 통합하는 과정을 보여준다. 또한 분석 데이터를 추출하기 위한 웹마이닝 과정과 추출된 분석 데이터가 데이터베이스에 저장되는 과정을 보여준다.

• Design Convergence Study
• /
• v.16 no.3
• /
• pp.17-37
• /
• 2017

The purpose of this study is to evaluate the value of the center of age-friendly goods to confirm the public value of the center. 152 elderly participants, either who have utilized the center themselves or who have experienced the center indirectly through the image have participated in the study. The experience of age-friendly product center was evaluated by the contingent valuation method, which is widely used in the public services. A double-bounded dichotomous choice (DB-DC) log-logistic model and a log-normal model were conducted. As the result of the estimation, the truncated mean of the log-logistic model was 3,401 Korean won, with the mean of 4,937 won. In addition, the truncated mean of the log-normal model was 3,433 won, with the mean of 4,144 won. Elderly who have not experienced the center placed a higher value on the center, compared to the experienced elderly. The results imply the necessity to enlarge the opportunity for future suppliers to experience the center for age-friendly products, in order to improve their objective understandings. In addition, the necessity of expanding exhibition products and applying age-friendly designs is proposed, to offer various attractive experiences to elderly.

• Journal of Korean Society of Transportation
• /
• v.25 no.5
• /
• pp.135-148
• /
• 2007

Understanding the relation between characteristics of an accident and its duration is crucial for the efficient response of accidents and the reduction of total delay caused by accidents. Thus the objective of this study is to model accident duration using an AFT metric model. Although the log-logistic and log-normal AFT models were selected based on the previous studies and statistical theory, the log-logistic model was better fitted. Since the AFT model is commonly used for the purpose of prediction, the estimated model can be also used for the prediction of duration on freeways as soon as the base accident information is reported. Therefore, the predicted information will be directly useful to make some decisions regarding the resources needed to clear accident and dispatch crews as well as will lead to less traffic congestion and much saving the injured.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.9 no.3
• /
• pp.306-311
• /
• 2016

Software reliability in the software development process is an important issue. Infinite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, reliability software cost model considering logarithmic and exponential fault detection rate based on observations from the process of software product testing was studied. Adding new fault probability using the Goel-Okumoto model that is widely used in the field of reliability problems presented. When correcting or modifying the software, finite failure non-homogeneous Poisson process model. For analysis of software reliability model considering the time-dependent fault detection rate, the parameters estimation using maximum likelihood estimation of inter-failure time data was made. The logarithmic and exponential fault detection model is also efficient in terms of reliability because it (the coefficient of determination is 80% or more) in the field of the conventional model can be used as an alternative could be confirmed. From this paper, the software developers have to consider life distribution by prior knowledge of the software to identify failure modes which can be able to help.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.1
• /
• pp.1-9
• /
• 2009

Let us consider that the variance function in regression model has a discontinuity/change point at unknown location. Yu and Jones (2004) proposed the local polynomial fit to estimate the log-variance function which break the positivity of the variance. Using the local polynomial fit, Huh (2008) estimate the discontinuity point of the log-variance function. We propose a test for the existence of a discontinuity point in the log-variance function with the estimated jump size in Huh (2008). The proposed method is based on the asymptotic distribution of the estimated jump size. Numerical works demonstrate the performance of the method.

• The Korean Journal of Applied Statistics
• /
• v.30 no.2
• /
• pp.259-269
• /
• 2017

Due to the nonnegativity of variance, most of nonparametric estimations of discontinuous variance function have used the Nadaraya-Watson estimation with residuals. By the modification of Chen et al. (2009) and Yu and Jones (2004), Huh (2014, 2016a) proposed the estimators of the log-variance function instead of the variance function using the local linear estimator which has no boundary effect. Huh (2016b) estimated the variance function using the adjusted squared residuals by the estimated jump size in the discontinuous variance function. In this paper, we propose an estimator of the discontinuous log-variance function using the local linear estimator with the adjusted log-squared residuals by the estimated jump size of log-variance function like Huh (2016b). The numerical work demonstrates the performance of the proposed method with simulated and real examples.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.11
• /
• pp.53-63
• /
• 2023

This study developed a cognitive impairment predictive model as one of the screening tests for preventing dementia in the elderly by using Automated Machine Learning(AutoML). We used 'Wearable lifelog data for high-risk dementia patients' of National Information Society Agency, then conducted using PyCaret 3.0.0 in the Google Colaboratory environment. This study analysis steps are as follows; first, selecting five models demonstrating excellent classification performance for the model development and lifelog data analysis. Next, using ensemble learning to integrate these models and assess their performance. It was found that Voting Classifier, Gradient Boosting Classifier, Extreme Gradient Boosting, Light Gradient Boosting Machine, Extra Trees Classifier, and Random Forest Classifier model showed high predictive performance in that order. This study findings, furthermore, emphasized on the the crucial importance of 'Average respiration per minute during sleep' and 'Average heart rate per minute during sleep' as the most critical feature variables for accurate predictions. Finally, these study results suggest that consideration of the possibility of using machine learning and lifelog as a means to more effectively manage and prevent cognitive impairment in the elderly.

• The Korean Journal of Applied Statistics
• /
• v.33 no.1
• /
• pp.107-114
• /
• 2020

Extreme value distributions have often been used for the analysis (e.g., prediction of return level) of data which are observed from natural disaster. By the extreme value theory, the block maxima asymptotically follow the generalized extreme value distribution as sample size increases; however, this may not hold in a small sample case. For solving this problem, this paper proposes the use of a log-logistic (LLG) distribution whose validity is evaluated through goodness-of-fit test and model selection. The proposed method is illustrated with data from annual maximum earthquake magnitudes of China. Here, we present the predicted return level and confidence interval according to each return period using LLG distribution.