• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.127-130
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• 2003

주변분포가 Laplace 분포인 세 가지 형태의 이변량 Laplace 분포를 연구한다. 각각의 이변량 Laplace 분포의 확률밀도함수와 누적분포함수를 유도하고, 분포의 그래프를 그려봄으로써 분포의 형태를 알아본다. 조건부 적률을 정리하여 조건부 첨도와 조건부 왜도를 구하고 분포의 성질을 파악한다. 상관계수를 구하여 다른 이변량 분포의 상관계수와 비교해 보았다. 그리고 정의된 분포함수를 응용하여 이변량 Laplace 분포를 따르는 난수벡터를 발생하는 알고리즘을 제안하였으며, 생성된 난수벡터의 표본으로부터 구한 표본평균과 중앙값의 분산-공분산 행렬식을 구하고 이변량 정규분포에 대응하는 행렬식과 비교 토론하였다.

• Journal of Conservation Science
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• v.33 no.2
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• pp.75-83
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• 2017

This study produced smelting slag through the reproduction of an ancient iron manufacturing technique, with the aim of facilitating a comprehensive understanding of the process by analyzing the slag components. The research suggests an interpretation method using the ratio of the subcomponents relative to the main slag components as an alternative to existing methods. We investigated the component source within the smelting furnace from which the slag is derived by developing an understanding of the tendency between slags. Based on bivariate graph and triangular coordinate data analysis, it was found that a slag can be categorized according to its components. The groups were identified as the ore slag group(centered on the ore), and the clay slag group(centered on clay and granite soil). This research determined that it is possible to estimate the components derived from the slag, depending on which group they belong to or resemble, as shown in Figure 4~7. It was found that a comprehensive understanding of the ratio between the components was more accurate than a simple analysis of the contents, for the interpretation of ancient iron manufacturing processes. This is based on the fact that a higher ratio of $TiO_2$ was detected by the components analysis, and an analysis of all the slag showed that the value of $CaO/SiO_2$ ratio was lower than 0.4, which corresponds to the reproduction experiment condition in which flux was not used.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.163-170
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• 2006

The argument concerning the choice of effect measure for epidemiologic data or clinic data has been renewed. But the relationships among effect measures can be confusing if effect measures are expressed by conventional mathematical functions alone. In this article, risk difference(RD), relative risk(RR), and odds ratios(OR) for binary data are presented by radar diagram instead of mathematical functions and the relationships among them are showed using radar diagram. This radar diagram is offered flexible conceptual tool to understand effect measures, DR, RR, and OR for binary data.