• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.1057-1068
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• 2003

Results of using principal component analysis prior to cluster analysis are compared with results from applying agglomerative clustering algorithm alone. The retrieval ability of the agglomerative clustering algorithm is improved by using principal components prior to cluster analysis in some situations. On the other hand, the loss in retrieval ability for the agglomerative clustering algorithms decreases, as the number of informative variables increases, where the informative variables are the variables that have distinct information(or, necessary information) compared to other variables.

• 한국인쇄학회지
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• 제30권2호
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• pp.13-22
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• 2012

This research attempts to analyze communication effects of print ad having pictorial typography. 150 Questionnaires were distributed to respondents staying Daejeun City and 148 copies were retreated for five days from April 22nd to 26th, 2012. Frequency analysis, factor analysis, Cronbach's alpha for reliability analysis were utilized for data analysis with SPSS 12.0. For testing hypothesis, regression analysis was used. As result of testing hypothesis, 'informative, beneficial, creative, reliable' were partially significant to attitude towards print ad having pictorial typography. That means 'creative' and 'reliable' were insignificant, while 'informative' and 'beneficial' are significant. Variable of the most influencing on attitude towards advertising is 'informative.' 'Informative, beneficial, creative, and reliable' were partially significant to brand attitude, too. That means 'beneficial' and 'creative' were insignificant, while 'informative' and 'reliable' were significant. Variable of the most influencing on brand attitude was 'reliable.' Therefore, to enhance communication effect of print ad having pictorial typography, 'informative' and 'reliable' are most significant variables.

• 아태비즈니스연구
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• 제8권1호
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• pp.15-32
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• 2017

This study was designed to examine the antecedents of mobile app usage among smart phone users. An extended TAM, which included the additional factors of perceived enjoyment, perceived informative usefulness and perceived social usefulness, was applied to predict people's intention to use mobile apps. Overall, the hypothesized research model did a fairly good job explaining significant associations between the independent variables and the dependent variable. The findings had showed that perceived social usefulness, perceived enjoyment and attitude were significantly affect intention to use mobile apps. Meanwhile, perceived ease of use (PEOU) and perceived informative usefulness were not significantly effects attitude towards intention to use mobile apps. Therefore, mobile apps developers should develop mobile apps that are easier for the users to seek information. For the information available should be more precise and bringing more benefits to the users.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2001년도 추계학술발표회 논문집
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• pp.89-94
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• 2001

In this study we focus on variable selection in decision tree growing structure. Some of the splitting rules and variable selection algorithms are discussed. We propose a competitive variable selection method based on Kruskal-Wallis test, which is a nonparametric version of ANOVA F-test. Through a Monte Carlo study we note that CART has serious bias in variable selection towards categorical variables having many values, and also QUEST using F-test is not so powerful to select informative variables under heavy tailed distributions.

• 복식
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• 제63권2호
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• pp.1-13
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• 2013

The purpose of this study is to examine how the features of network in the Internet fashion community affect the diffusion of fashion information to members in the online community with other variables (informative features, consumer features). Communities that actively exchange fashion information among their members were selected for the social network analysis and hypothesis verification. As a result, we found that a few information activists influenced most of the information receivers in the network features of fashion communities. Also, we found that the informative features (usefulness, reliability), consumer features (NFC, innovation) as well as the network features (connectivity, power), have a significant influence on the diffusion of Internet fashion information which verified the importance of the network features in the study on the Internet.

• 한국산학기술학회논문지
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• 제10권12호
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• pp.3824-3832
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• 2009

본 연구에서는 과연 근무시간에 '블로그' 사용하는 것이 업무의 성과에 도움을 주는 것일까? 라는 질문에 대한 해답을 구하고자 하였다. 이를 위해 다음을 분석하였다. 첫째, 블로그 사용의도 즉, 상호작용성, 정보추구성, 오락 추구성 등을 독립변수로, 업무만족도를 종속변수로 설정하고 연구모형을 수립하고 블로그 사용요인과 업무 만족도의 관계를 분석하였다. 둘째, 근무시간에 블로그를 사용하는 이유는 업무특성에 따라 다를 수 있다는 가정 아래 업무특성 요인을 정형/비정형업무, 개인업무/집단업무, 재석업무/이석업무로 구분지었으며, 각각의 종사자들의 응답을 그룹화지어 더미변수화 하여 독립변수(블로그 사용요인)와 종속변수(업무만족도)에 조절효과가 있는지 분석하였다. 분석결과, 블로그 사용요인과 업무만족도의 회귀분석 결과 블로그 사용요인 중 상호작용성과 정보추구성은 업무만족도에 영향을 줄 수 있지만, 오락추구성은 업무만족도에 그다지 영향을 미치지 않는 것으로 분석되었다. 둘째, 업무특성에 따라 블로그 사용 의도는 다를 수 있다는 가정 아래 업무특성 요인이 블로그 사용의도에 어떠한 영향을 주는지에 대한 결과는 응답자의 결과 값을 정형/비정형업무, 개인업무/집단업무, 재석업무/이석업무로 구분 지었으며, 회귀분석을 돌렸지만 이러한 업무특성요인에 따른 블로그 사용의도는 업무만족도에 별다른 영향을 주지 않는 것으로 드러났다. 이러한 결과를 바탕으로 학문적, 실무적 시사점을 제시하였다.

• Communications for Statistical Applications and Methods
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• 제17권5호
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• pp.689-696
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• 2010

구간중도 절단자료는 감염 자료, 종양 발생 자료등 그 발생 시간을 정확하게 관측할 수 없는 경우에 흔히 발생되는 자료로 정확한 사건 발생 시간대신에 발생 전 마지막 관측시점과 발생 후 첫 번째 관측시점으로 구성된다. 이러한 종류의 자료는 Sun (2006)에 의해 자세하게 논의되었으며 관측 개체간의 독립성 가정 하에서 여러 가지 방법들에 의해 분석되어져 왔다. 본 논문에서는 관측 개체들이 군집으로부터 발생하여 더 이상독립성 가정이 적절하지 못한 경우를 고려한다. 특히 반응변수인 사건 발생 시간이 군집의 크기와 연관되어 있을 때, 이를 고려하기 위한 결합 모형을 제시한다. 제안된 모형은 림프계 필라리아병의 실제 자료에 적용한다.

• 응용통계연구
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• 제24권6호
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• pp.1213-1224
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• 2011

관측치의 개수보다 변량의 개수가 더 많은 다변수 상황에서 정규혼합모형을 이용하여 군집분석을 하기 위해서는 비정보적인 변수들을 제거하는 과정이 필수적으로 요구된다. 이와 같은 변수선택과 군집의 동시 처리를 위한 기존 연구의 대부분은 군집별 등분산 가정 하에서 이루어져 왔으며, 비정보적인 변수를 제거하기 위해 주로 벌점화 우도 기법이 이용되었다. 본 연구에서는 약간 변형된 정규혼합모형을 기반으로 비현실적인 등분산 가정을 탈피하면서 효율적으로 비정보적인 변수를 제거하는 새로운 방법을 제공한다. 이 모형에 대한 타당성을 설명하였고, 모수 추정을 위한 EM 알고리즘을 유도하였다. 그리고 모의실험 및 실자료 실험을 통해 제안된 방법의 유효성을 보였다.

• 대한가정학회지
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• 제41권3호
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• pp.93-111
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• 2003

This study aimed to examine the water consumption of urban households according to pro-environmental attitude for environmental protection. In contrast to preview studies, this study purposed to include various related independent variables, motive to environmental behavior, in special, in the model, and suggest informative data for research, education and strategies related to environmental protection. The data were from 665 housewives living in five urban areas. For the analysis of data, frequencies, means, percentages, GLM analysis, DMR test and Chi-square test were used. The main results of this study were as follows; 1. The respondents held high pro-environmental attitude that pro-environmental behaviors are important to protect environment. The pro-environmental attitude among the respondents were statistically different from the independent variables : spouse's occupation, living area, help of housekeeper, knowledge about environmental protection, convenience to check water consumption, and perception of voluntary conservative behavior among neighborhood 2. There were great difference on water consumption among respondents. The significant independent variables to have effects on water consumption were different between water consumption per person and higher/lower average water consumption. The relationships of pro-environmental attitude and motive to pro-environmental behavior with two water consumption as dependent variables were unique. Also, living areas and knowledge about environment protection were consistently important to explain the difference of water consumption.

• Bulletin of the Korean Chemical Society
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• 제33권5호
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• pp.1527-1535
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• 2012

Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms.