• 응용통계연구
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• 제26권6호
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• pp.933-942
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• 2013

본 연구에서는 시뮬레이션 방법을 사용해서 다양한 조건에서 주성분분석이 얼마나 잘 요인 구조를 복원할 수 있는지를 공통요인분석과 비교하여 체계적으로 평가하였다. 이 연구에서 요인 대 변수 비율, 공통성, 그리고 표본크기를 실험변수로 설정하였다. 주성분분석은 표본의 크기가 200개 이하인 경우 공통적으로 공통요인분석에 비해 더 우수한 요인구조의 복원력을 보여주었다. 특히, 요인 당 변수 수가 적은 경우, 주성분분석은 50개의 표본에서도 만족할 만한 수준의 요인복원능력을 보여주었다. 이와 더불어 공통성 수준 또한 낮은 경우 필요한 표본수는 100개로 늘어난다. 본 연구결과는 요인추출방법으로서 주성분분석의 선택의 근거를 제시하고 타당한 사용에 관한 가이드라인을 제시해 준다.

• Journal of the Korean Statistical Society
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• 제25권1호
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• pp.67-80
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• 1996

Factor analysis is a multivariate technique for describing the in-terrelationship among many variables in terms of a few underlying but unobservable random variables called factors. There are various approaches for this factor analysis. In particular, principal factor analysis is one of the most popular methods. This follows the mathematical algorithm of the principal component analysis based on the singular value decomposition. But it is known that the singular value decomposition is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, using the resistant singular value decomposition of Choi and Huh (1994), we derive a resistant principal factor analysis relatively little influenced by notable observations.

• 한국환경과학회지
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• 제7권2호
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• pp.171-176
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• 1998

This study was conducted to evaluate water quality utilizing principal component analysis in the Nakdong River Estuary. From the results of analysis, water quality in the Nakdong River Estuary could be explained up to 65.3 Percente by three factors which were Included In river loadlnwastes from the Nakdong River and rainfalls : 39.1%1, sediment resuspension(13.7BS) and metabolism(12.5%). In the eastern part of estuary In flowing the Nakdong River, river loading factor score(factor 1 Pas higher than that In western part. Sediment resuspension factor score(factor 2) was high in shallow water, while metabolism factor score(factor 3) was high in deeper water. For seasonal variations of factors score, factor 1 was h19h- 1y related to rainfall season.

• 농촌계획
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• 제12권1호
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• pp.37-48
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• 2006

The objectives of this study were to develop an evaluation method of regional vulnerability to agricultural drought and to classify the vulnerability patterns. In order to test the method, 24 city or county areas of Gyeonggi-do were chose. First, statistic data and digital maps referred for agricultural drought were defined, and the input data of 31 items were set up from 5 categories: land use factor, water resource factor, climate factor, topographic and soil factor, and agricultural production foundation factor. Second, for simplification of the factors, principal component analysis was carried out, and eventually 4 principal components which explain about 80.8% of total variance were extracted. Each of the principal components was explained into the vulnerability components of scale factor, geographical factor, weather factor and agricultural production foundation factor. Next, DVIP (Drought Vulnerability Index for Paddy), was calculated using factor scores from principal components. Last, by means of statistical cluster analysis on the DVIP, the study area was classified as 5 patterns from A to E. The cluster A corresponds to the area where the agricultural industry is insignificant and the agricultural foundation is little equipped, and the cluster B includes typical agricultural areas where the cultivation areas are large but irrigation facilities are still insufficient. As for the cluster C, the corresponding areas are vulnerable to the climate change, and the D cluster applies to the area with extensive forests and high elevation farmlands. The last cluster I indicates the areas where the farmlands are small but most of them are irrigated as much.

• 수산해양교육연구
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• 제28권2호
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• pp.465-472
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• 2016

본 연구에서는 2010년부터 2014년까지 최근 5년간 우리 나라 주변해역에서 발생하여 중앙해양안전심판원의 재결을 마친 1417건의 해양사고에 대해 이를 25개 요인별로 분류하고, SPSS 통계 프로그램에 의한 주성분분석(Principal Component Analysis; PCA)을 행하여 이들 각 요인들의 상관성 및 주요 해양원인을 분석 고찰하였다. 얻어진 주요한 결과들을 요약하면 다음과 같다. 1. 해양사고의 주된 원인은 기관설비취급불량, 화기취급불량, 항행법규소홀, 침로선정유지불량, 경계소홀 등 기관실 및 조타실 관련 인적요인에 의해 발생한다. 2. 조타실 관련 인적요인에 의해 발생하는 사고는 충돌과 좌초 등이 큰 비중을 차지하며, 기관실 관련 인적요인에 의해 발생하는 사고유형은 주로 기관손상이나 화재폭발 등이다. 3. 주성분분석의 결과 제1주성분은 해양사고의 출현율을, 제2주성분은 해양사고의 원인을, 제3주 성분은 해양사고의 유형을 나타낸다.

• 한국지능시스템학회논문지
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• 제25권3호
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• pp.293-298
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• 2015

해상교통상황에서 수집된 선박 조우(Encounter) 데이터 변수는 선박 충돌 및 근접사고(Near-Collision) 위험도를 통계적인 방법에 의한 분석이 가능하다. 본 연구에서는 선박 조우 데이터에서 추출되는 다수의 선박충돌위험도 평가 변수들을 요인분석(Factor Analysis)하여, 선박 조우데이터에서 충돌위험에 영향을 미치는 주요 요인을 결정하고자 한다. 각 요인 결정을 위해 선박조우데이터 변수 정규분포화 및 표준화를 수행한 후 주성분 분석(Principal Component Analysis)으로 요인을 결정하였다. 요인분석결과 선박 근접도 요인과 충돌회피변화요인으로 요약하였다.

• 대한가정학회지
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• 제36권12호
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• pp.43-54
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• 1998

From the viewpoint of clothing construction, it is necessary to grasp exactly the shapes of the neck and the shouder, such as the line of the neck base, the neck gradient, the shoulder gradient, the shape of the scapular, and the shape of the breast. In this report, factor analysis was applied to 39 items of neck & shoulder level measurements, including stature, weight, but grith, waist girth, to demonstrate the most relevant measurements for collar and bodice pattern designing, and to classify the neck and shoulder level shapes. The subjects investigated were 126 women of the age 20-29. The main results are follows : 1. For factors of body form were extracted by the factor analysis. The 1st principal component can be interpreted as "size" component, the 2nd-3th principal component is "shape" component relating to neck and shoulder level, and the 4th principal component is "shoulder shape" component. 2. With regard to factor loadings, we were able to extract the most relevant measurements for collar and bodice pattern designing. M16, M22, S26, S30, S34, S35, S36, C37, C38, C39.

• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.751-759
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• 2008

Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.

• Journal of applied mathematics & informatics
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• 제14권1_2호
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• pp.377-386
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• 2004

A technique that provides prediction intervals based on a model called an empirical linear model is discussed. The technique, high-dimensional empirical linear prediction (HELP), involves principal component analysis, factor analysis and model selection. HELP can be viewed as a technique that provides prediction (and confidence) intervals based on a factor analysis models do not typically have justifiable theory due to nonidentifiability, we show that the intervals are justifiable asymptotically.

• Journal of the Korean Data and Information Science Society
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• 제15권3호
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• pp.531-542
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• 2004

Principal component analysis and cluster analysis were conducted to comprehensively evaluate the water quality of Busan coastal area with the data collected seasonally by the analysis of surface water at 10 stations from 1997 to 2003. We noted that the first principal component was regarded as a factor related with the input of nutrient-rich fresh water and the second principal component as meteorological characteristics. Also we obtained that water qualities of station 4 and 9 were different from those of other stations in Busan coastal area.