• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4B
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• pp.319-328
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• 2009

This study quantified the data by applying the EDA techniques considering the data structure, and the results were then used for the frequency analysis. Although traditional methods based on the method of moments provide very sensitive statistics to the extreme values, the EDA techniques have an advantage of providing very stable statistics with their small variation. For the application of the EDA techniques to the frequency analysis, it is necessary to normalization transform and inverse-transform to conserve the skewness of the raw data. That is, it is necessary to transform the raw data to make the data follow the normal distribution, to estimate the statistics by applying the EDA techniques, and then finally to inverse-transform the statistics of transformed data. These statistics decided are then applied for the frequency analysis with a given probability density function. This study analyzed the annual maxima one hour rainfall data at Seoul and Pohang stations. As a result, it was found that more stable rainfall quantiles, which were also less sensitive to extreme values, could be estimated by applying the EDA techniques. This methodology may be effectively used for the frequency analysis of rainfall at stations with especially high annual variations of rainfall due to climate change, etc.

• The Journal of the Korea Contents Association
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• v.12 no.8
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• pp.484-495
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• 2012

The purpose of this study was to investigate the effects of game-based-digital textbook on the basic arithmetic abilities and the task attention of students with mental retardation. To do this, 38 students with mental retardation participated and were assigned to the three groups. The first group only used the traditional text book, the second group only used the game-based-digital textbook, and the third group used both the traditional textbook and the game-based-digital textbook. The third group using both the traditional textbook and the game-based-digital textbook revealed more higher improvement than the other two groups in the basic arithmetic and the task attention.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1045-1054
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• 2011

During clinical trials a researcher is frequently able to observe a disease symptom in a subject as well as a severity score for those who experienced a symptom after a fixed length of treatment. The traditional method to evaluate a decreasing trend in proportion, when there is an intrinsic order in the treatment groups (for example control and two or more treatment groups) is a Cochran-Armitage test, while the method to evaluate a decreasing trend in continuous non-normal data is a Jonckheere-Tersptra test. The Cochran-Armitage test emphasizes the dichotomous data of symptom occurrence and the Jonckheere-Tersptra test emphasizes the continuous non-normal data of severity symptom scores. In this paper we propose new test statistics that consider the combined evidence from a symptom occurrence and disease severity score. We illustrate these methods with example data of schizophrenic inpatients that demonstrated antipsychotic-drug induced constipation. A small-scale simulation is conducted to compare the new trend tests with other trend tests.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.443-447
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• 2022

The purpose of this study attempted to investigate academic achievement and cooperative ability as problem-based learning to strengthen communication in Spanish courses. 70 learners taking Spanish at A University from September to December, 2021 were divided into an experimental and a control class to examine their academic achievement and cooperate ability. Academic achievement was not statistically significant, but the sub-area of communication was significant as 0.031 (*p<.05) and the cooperative ability of the experimental group, the average was increased, but it was not statistically significant. Based on this study, continuous follow-up research to develop teaching methods and teaching and learning models suitable for strengthening Spanish communication through various learner-centered convergence teaching methods will contribute to fostering talent desired by the 21st century.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.41-68
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• 2019

Multi-view learning considers data from various viewpoints as well as attempts to integrate various information from data. Multi-view learning has been studied recently and has showed superior performance to a model learned from only a single view. With the introduction of deep learning techniques to a multi-view learning approach, it has showed good results in various fields such as image, text, voice, and video. In this study, we introduce how multi-view learning methods solve various problems faced in human behavior recognition, medical areas, information retrieval and facial expression recognition. In addition, we review data integration principles of multi-view learning methods by classifying traditional multi-view learning methods into data integration, classifiers integration, and representation integration. Finally, we examine how CNN, RNN, RBM, Autoencoder, and GAN, which are commonly used among various deep learning methods, are applied to multi-view learning algorithms. We categorize CNN and RNN-based learning methods as supervised learning, and RBM, Autoencoder, and GAN-based learning methods as unsupervised learning.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.255-263
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• 2024

GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID.

• Journal of Korea Water Resources Association
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• v.32 no.4
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• pp.437-445
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• 1999

The present paper describes the problem of hydrologic response estimation using non-parametric ridge regression method. The method adapted in this work is based on the minimization of the $C_L$ statistics, which is an estimate of the mean square prediction error. For this method, effects of using both the identity matrix and the Laplacian matrix were considered. In addition, we evaluated methods for estimating the error variance of the impulse response. As a result of analyzing synthetic and real data, a good estimation was made when the Laplacian matrix for the weighting matrix and the bias corrected estimate for the error variance were used. The method and procedure presented in present paper will play a robust and effective role on separating hydrologic response.

• Korean Journal of Head & Neck Oncology
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• v.16 no.2
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• pp.177-181
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• 2000

배경 및 목적: Theodor Kocker에 의해 일반화된 전통적인 갑상선 수술방법은 갑상선 질환의 종류 및 정도, 수술범위와 상관없이 광범위한 수술범위로 인한 조직 손상으로 인해 수술후 환자들의 여러가지 불편감은 물론 경부의 넓은 부위의 통증과 경부 피부부종, 장액종, 혈종 등과 같은 후유증을 동반할 수 있다. 최근 본 저자들은 이같은 전통적 갑상선 수술의 부작용을 최소화하기 위해 작은 피부절개($3{\sim}4.5cm$) 후 피하 피판(subplatysmal skin flap) 없이 직접 갑상선으로 접근하는 새로운 수술기법으로서 최소침습 갑상선 수술기법을 개발하였기에 그 술식을 소개하고 전통적인 갑상선 절제술에 대한 우월성을 확인하고자 본 연구를 시행하였다. 대상 및 방법: 1999년 1월 15일 부터 2000년 1월 14일까지 573예의 갑상선 수술 예 중 최소침습 갑상선절제술이 시행되었던 466예와 1998년 1월 15일부터 1999년 1월 14일까지 전통적 갑상선 수술을 시행한 549예 중 거대 종양(양성>6cm, 악성>5cm), 흉골하 선종, 국소진행암, 재발암, 측경부의 다발성 림프절 전이가 있었던 112예를 제외한 437예의 임상병리적 특성과 피부절개 길이, 수술 시간, 수술중 출혈양, 수술후 진통제 요구빈도 및 재윈기간, 수술 후 합병증 발생빈도를 비교 분석하였다. 결 과: 두 군간의 임상병리적 특성상의 유의한 차이는 없었다. 피부절개 길이($3.7{\pm}0.7cm,\;vs\;9.6{\pm}3.3cm$), 수술 시간($57.6{\pm}11.7$분 vs $85.2{\pm}32.3$분) 수술 중 출혈양($18.4{\pm}15.3ml\;vs\;43.1{\pm}21.8ml$), 수술후 재원기간($1.6{\pm}0.5$일 vs $4.3{\pm}1.6$일), 및 수술후 진통제 요구빈도가 전통적 수술군에 비해 최소침습 수술군에서 통계적으로 유의하게 감소되었으나(p<0.05), 수술후 장액종 및 혈종 형성, 일시적인 음색변화, 일시적인 저칼슘혈증과 같은 합병증의 발생빈도는 각각 4.3%(n=20)와 4.8%(n=21)로 두 군간에 유의한 차이가 없었다. 결 론: 최소침습 갑상선 수술법은 새로운 수술기구의 도입 없이도 갑상선 수술의 충분한 시야를 확보할 수 있고 안전하고 간단하게 시행할 수 있으며, 기존 수술법으로 인한 부작용을 최소화할 수 있어 전통적 인 수술법을 대치할 수 있는 새로운 방법으로 사료된다.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.135-140
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• 2007

이탈 고객 예측은 데이터 마이닝에서 다루는 주요한 문제 중에 하나이다. 이탈 고객 예측은 일종의 분류(classification) 문제로 의사결정나무추론, 로지스틱 회귀분석, 인공신경망 등의 기법이 많이 활용되어왔다. 일반적으로 이탈 고객 예측을 위한 모델은 고객의 인구통계학적 정보와 계약이나 거래 정보를 입력변수로 하여 이탈 여부를 목표변수로 보는 형태로 분류 모델을 생성하게 된다. 본 연구에서는 고객과의 지속적인 접촉으로 발생되는 추가적인 사건 정보를 활용하여 연관성 규칙을 생성하고 이 결과를 기존의 방식으로 생성된 분류 모델과 결합하는 이탈 고객 예측 방법을 제시한다. 제시한 방법의 유용성을 확인하기 위해서 특정 국내 신용카드사의 실제 데이터를 활용하여 실험을 수행하였다. 실험 결과 제시된 방법이 기존의 전통적인 분류 모델에 비해서 향상된 성능을 보이는 것을 확인할 수 있었다. 제시된 예측 방법의 장점은 기존의 이탈 예측을 위한 입력 변수들 이외에 고객과 회사간의 접촉을 통해서 생성된 동적 정보들을 통합적으로 활용하여 예측 정확도를 높이고 실시간으로 이탈 확률을 갱신할 수 있다는 점이다.