• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.231-242
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• 1995

In this paper we consider kernel type estimator of the spectral density at a point in the analysis of stationary time series data. The kernel entails choice of smoothing parameter called bandwidth. A data-based bandwidth choice is proposed, and it is obtained by solving an equation similar to Sheather(1986) which relates to the probability density estimation. A Monte Carlo study is done. It reveals that the spectral density estimates using the data-based bandwidths show comparatively good performance.

• Journal of the Korean Data and Information Science Society
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• 제16권2호
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• pp.289-300
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• 2005

In this article quick variance change point (VCP) detection problem for time series is considered. For this variance VCP detector equipped with tuning parameters is proposed. A major tool for the detector is moving variance ratio (MVR) which monitors variance change of a given time series. Tuning process of detector is investigated via simulation, which shows that tuning parameters are critical in achieving sensitivity and adaptiveness of detector.

• 정보관리학회지
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• 제26권3호
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• pp.417-434
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• 2009

국가도서관통계시스템에서 요구하는 통계항목들은 이론적 견지에서 보면 도서관평가 또는 도서관운영 정책수립을 위해 필요한 데이터를 기반으로 한 항목들이라고 할 수 있다. 그러나 국가도서관통계시스템에서 요구하는 통계항목과 공공도서관현장에서 일반적으로 수집될 수 있는 통계항목들과는 차이가 있음을 알 수 있다. 따라서 공공도서관 평가로 활용되고 있는 국가도서관통계가 신뢰할 수 있는 또는 타당성이 있는 객관적 데이터인지를 알아보기 위하여, 공공도서관 현장에서 통계를 수집할 수 있는 근거자료인 도서관 업무(운영)일지의 항목들을 분석하고 아울러 통계의 자동수집이 가능한 도서관리 프로그램에 대한 공공도서관에서의 사용 현황을 조사하였다. 이 연구의 분석 자료는 향후 공공도서관 업무(운영)일지의 표준안 마련을 위한 기초 자료로 활용될 것이다.

• 디지털융복합연구
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• 제15권2호
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• pp.89-98
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• 2017

본 연구는 인과관계 분석에서 주로 활용되는 SPSS statistics(회귀분석)과 구조방정식모델을 구현하는 프로그램 중 하나인 AMOS 프로그램을 각각 활용하여 동일한 데이터에 대하여 조절효과 검정을 위한 실증분석을 실시하였다. 실증분석 결과, SPSS statistics을 활용한 회귀분석에서 상황변수가 범주형데이터인 성별과 연속형데이터인 컨설팅만족도 모두에서 조절효과가 없는 것으로 나타난 반면, AMOS 프로그램을 활용한 구조방정식모델에서는 10% 유의수준에서 컨설턴트의 능력 및 태도가 컨설팅재구매에 미치는 영향관계를 컨설팅만족도가 부분적으로 조절하고 있는 것으로 나타났다. 결국, 조절효과 분석은 AMOS 프로그램을 활용한 구조방정식모델과 SPSS statistics을 활용한 회귀분석모델이 전혀 다른 접근방법을 사용하고 있어 얼마든지 상이한 결과가 나올 수 있음을 보여준다.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.237-245
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• 2003

MDS(multi-dimensional scaling) for preference data is a graphical tool which usually figures out how consumers recognize, evaluate certain products. This article is mainly concerned with an optimal scaling for MDS when target configuration is available. Rotation of axis and SUR(seemingly unrelated regression) methods are employed to get a new configuration which is obtained as close to the target as we can. Methodologies developed here are also illustrated via a real data set.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.557-566
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• 2005

Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper we used k-means clustering of several clustering techniques. The k-means Clustering Is classified as a partitional clustering method. We analyze 2002 Gyeongnam social indicator survey data using k-means clustering techniques for environmental information. We can use these outputs given by k-means clustering for environmental preservation and environmental improvement.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.1145-1155
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• 2007

In this paper, we consider the permutation tests for the multivariate data under the two-sample problem setting. We review some testing procedures, which are parametric and nonparametric and compare them with the permutation ones. Then we consider to try to apply the permutation tests to the multivariate data having the continuous and discrete components together by choosing some suitable combining function through the partial testing. Finally we discuss more aspects for the permutation tests as concluding remarks.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권1호
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• pp.36-41
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• 2009

Self organizing map(SOM) is one of the unsupervised methods based on the competitive learning. Many clustering works have been performed using SOM. It has offered the data visualization according to its result. The visualized result has been used for decision process of descriptive data mining as exploratory data analysis. In this paper we propose improvement of SOM using stratified sampling of statistics. The stratification leads to improve the performance of SOM. To verify improvement of our study, we make comparative experiments using the data sets form UCI machine learning repository and simulation data.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.149-160
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• 2006

Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

• 한국IT서비스학회지
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• 제23권3호
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• pp.55-63
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• 2024

To grasp the structure of an industry and monitor its changes, it is essential to utilize relevant statistics. Various statistics are being compiled regarding the software (SW) industry, presenting diverse numerical values. However, without a precise understanding of the scope and measurement methods inherent to each statistic, gaining a rigorous understanding of the industry's structure and evolving trends becomes challenging. Moreover, significant discrepancies between similar statistics often lead to confusion among users. In the software (SW) industry, key statistics commonly used include SW production value and SW market size. As of 2022, the annual domestic SW production value is reported as 77.4 trillion KRW (based on ICT Survey), while the SW market size for the same year is stated as 38.5 trillion KRW (according to IDC data). Although production value and market size may seem conceptually similar, there is approximately a twofold difference between the figures provided. Without understanding the meanings of each statistic and the differences between them, there are limitations in utilizing these statistics effectively. While statistics are utilized for various purposes such as policy development or causal analysis of policy using statistical raw data, research that presents and analyzes the precise meanings and limitations of each SW-related statistic is virtually non-existent. Thus, this study aims to compare and analyze the methodologies and differences among key statistics used to represent the SW industry: SW production value, SW market size, and SW GDP statistics. Through this analysis, the goal is to contribute to a better understanding of the SW industry's structure and enable more accurate and rigorous utilization of relevant statistics.