• Journal of the Korean Data and Information Science Society
• /
• 제15권4호
• /
• pp.707-716
• /
• 2004

Recently, many areas of application such as statistics and industrial engineering are interested in the effective education of databases. In this article we design and implement a database learning system based on learner management techniques. The system supports a personalized/ team-centered learning environment, monitoring the learning attitude of learners, and a method for the assessment.

• Journal of the Korean Data and Information Science Society
• /
• 제13권2호
• /
• pp.227-234
• /
• 2002

In this paper, we consider hierarchical Bayes generalized linear models for the analysis of longitudinal count data. Specifically we introduce the hierarchical Bayes random effects models. We discuss implementation of the Bayes procedures via Markov chain Monte Carlo (MCMC) integration techniques. The hierarchical Baye method is illustrated with a real dataset and is compared with other statistical methods.

• 한국신뢰성학회:학술대회논문집
• /
• 한국신뢰성학회 2000년도 춘계학술대회 발표논문집
• /
• pp.177-183
• /
• 2000

As monitoring, testing, and measuring techniques develop, predictive control of components and complete systems have become more practical and affordable. In this paper we develop a statistics-based approach assuming nonlinear degradation paths and time-dependent standard deviation. This approach can be extended to provide reliability estimates and limit value determination in the censoring case fur predictive maintenance policy.

• Journal of the Korean Data and Information Science Society
• /
• 제16권2호
• /
• pp.185-193
• /
• 2005

This study develops and introduces the program for letting learners study statistics in a systematic and efficient way by using Excel tools such as VBA and Macro, when they study statistical inference at two populations. This study helps learners understand the steps on statistical inference at two populations by utilizing the systematic and visual techniques.

• 한국빅데이터학회지
• /
• 제8권1호
• /
• pp.35-47
• /
• 2023

It is crucial to develop effective and efficient big data analytics methods for problem-solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.

• Communications for Statistical Applications and Methods
• /
• 제24권5호
• /
• pp.481-492
• /
• 2017

This paper investigates a convergence rate of a test statistics given by two scale sampling method based on $A\ddot{i}t$-Sahalia and Jacod (Annals of Statistics, 37, 184-222, 2009). This statistics tests for longitudinal data having the existence of long memory dependence driven by fractional Brownian motion with Hurst parameter $H{\in}(1/2,\;1)$. We obtain an upper bound in the Kolmogorov distance for normal approximation of this test statistic. As a main tool for our works, the recent results in Nourdin and Peccati (Probability Theory and Related Fields, 145, 75-118, 2009; Annals of Probability, 37, 2231-2261, 2009) will be used. These results are obtained by employing techniques based on the combination between Malliavin calculus and Stein's method for normal approximation.

• Journal of the Korean Data and Information Science Society
• /
• 제18권1호
• /
• pp.1-10
• /
• 2007

In this paper, we focus on churn analysis for the first successful candidates in the entrance examination on 2006 year using Clementine, data mining tool. The goal of this study is to apply decision tree including C5.0 and CART algorithms, neural network and logistic regression techniques to predict a successful candidate churn. And we analyze the churning and nochurning successful candidates and why the successful candidates churn and which successful candidates are most likely to churn in the future using data from entrance examination data of K university on 2006 year.

• 한국국방경영분석학회지
• /
• 제3권1호
• /
• pp.115-121
• /
• 1977

• 응용통계연구
• /
• 제5권1호
• /
• pp.81-91
• /
• 1992

통계학을 배우는 초보자들을 위하여, 퍼스날 컴퓨터를 이용한 통계교육용 소프트웨어(Computer Aided Teaching for Statistics : CATS)를 한글을 사용하여 개발하였다. 이 소프트웨어는 다량의 정보를 처리하기 보다는, 소량의 통계자료를 가지고 기초 통계학의 여러 기법을 실습하거나, 초보자가 책만 가지고 이해하기 힘든 이론을 컴퓨터를 이용하여 교육시키는데 주목적이 있다.

• Communications for Statistical Applications and Methods
• /
• 제25권2호
• /
• pp.199-215
• /
• 2018

Interest in $PM_{10}$ concentrations have increased greatly in Korea due to recent increases in air pollution levels. Therefore, we consider a forecasting model for next day $PM_{10}$ concentration based on the principal elements of air pollution, weather information and Beijing $PM_{2.5}$. If we can forecast the next day $PM_{10}$ concentration level accurately, we believe that this forecasting can be useful for policy makers and public. This paper is intended to help forecast a daily mean $PM_{10}$, a daily max $PM_{10}$ and four stages of $PM_{10}$ provided by the Ministry of Environment using various data mining techniques. We use seven models to forecast the daily $PM_{10}$, which include five regression models (linear regression, Randomforest, gradient boosting, support vector machine, neural network), and two time series models (ARIMA, ARFIMA). As a result, the linear regression model performs the best in the $PM_{10}$ concentration forecast and the linear regression and Randomforest model performs the best in the $PM_{10}$ class forecast. The results also indicate that the $PM_{10}$ in Seoul is influenced by Beijing $PM_{2.5}$ and air pollution from power stations in the west coast.