• Journal of Digital Convergence
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• v.13 no.10
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• pp.121-133
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• 2015

This tutorial presents an approach to perform the covariance based structural equation modeling using the R. For this purpose, the tutorial defines the criteria for the covariance based structural equation modeling by reviewing previous studies, and shows how to analyze the research model with an example using the "lavaan" which is the R package supporting the covariance based structural equation modeling. In this tutorial, a covariance-based structural equation modeling technique using the R and the R scripts targeting the example model were proposed as the results. This tutorial will be useful to start the study of the covariance based structural equation modeling for the researchers who first encounter the covariance based structural equation modeling and will provide the knowledge base for in-depth analysis through the covariance based structural equation modeling technique using R which is the integrated statistical software operating environment for the researchers familiar with the covariance based structural equation modeling.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.103-117
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• 2017

In longitudinal data analysis, the serial correlation of repeated outcomes must be taken into account using covariance matrix. Modeling of the covariance matrix is important to estimate the effect of covariates properly. However, It is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcome the restrictions, several Cholesky decomposition approaches for the covariance matrix were proposed: modified autoregressive (AR), moving average (MA), ARMA Cholesky decompositions. In this paper we review them and compare the performance of the approaches using simulation studies.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.489-496
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• 2018

This article is to examine the influence of occupational stress, socio-psychological factors and quality of sleep on fatigue symptoms from firefighters in fire service. The correlation coefficients were obtained by a tool of Pearson analysis, and covariance structure analysis was performed on the factors affecting the level of fatigue symptoms. This result suggests that the level of firefighters' fatigue symptoms in fire service. has a causal effect with occupational stress, socio-psychological factors and quality of sleep. Therefore, it is necessary to improve the work environment and to increase organizational support to deal with firefighters' fatigue in fire service.

• The Korean Journal of Financial Management
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• v.19 no.2
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• pp.27-48
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• 2002

본 논문은 이자율의 기간 구조가 차익 거래의 기회가 없도록 움직일 때 새로운 평균만기 측 정치인 AR 평균만기(arbitrage-free duration)을 도출하고 선물가격과 선도가격과의 관계를 분석한다. 지금까지 평균만기에 관한 많은 연구들은 수익률 곡선이 특정한 형태로 이동한다는 가정 하에서 평균만기를 유도하고 이에 근거하여 채권가격의 변동치를 측정하고 있다. 본 논문에서는 기존의 평균만기의 가정을 완화한 AR 평균만기를 도출하였다. 여기서 제시하는 AR 평균만기는 기존의 Macaulay 평균만기를 포함하는 일반화한 측정치라고 할 수 있다. 아울러 본 논문에서는 선물가격과 선도가격사이에 존재하는 이론적 관계를 규명하고자 하였다. 선물가격은 선도가격에 비해 할인된 가격이라는 것을 보이고 이자율 변동위험이 선물가격의 할인정도에 미치는 영향을 모형화 하였다. 최근 들어 선물을 이용한 채권 면역화에 대한 실증연구에 관심이 지속적으로 증가하고 있다. 전통적 실증연구 방법론에서는 먼저, 선물가격과 기초채권 가격사이에 존재하는 분산-공분산 행렬을 추정한다. 그런 후 추정된 분산-공분산 행렬을 바탕으로 이자율 위험 헤징 전략을 수립한 후 이 전략에 대한 실증 분석을 수행하였다. 그러나, 전통적 접근법의 가장 큰 문제는 비안정적(non-stationary)인 분산-공분산 행렬을 적절히 고려할 수 없었다는 점이다. 따라서, 본 연구의 결과를 기반으로 하면 최적의 헷징 전략을 수립하기 위한 이론적 기틀을 수립할 수 있을 것이다.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.1-9
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• 2010

This paper suggests a mixed-effects model for analyzing split-plot data when there is a repeated measures factor that affects on the response variable. Covariance structures are discussed among the observations because of the assumption of a repeated measures factor as one of explanatory variables. As a plausible covariance structure, compound symmetric covariance structure is assumed for analyzing data. The restricted maximum likelihood (REML)method is used for estimating fixed effects in the model.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.65-65
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• 2022

울창한 숲에도 어느 정도 햇빛은 들 듯이, 태양복사에너지는 식생의 잎과 흙에 모두 미치며, 그로 인해 증산과 증발이 각각 발생한다. 이러한 사실을 반영하는 것은 현존하는 증발산 산정 방법을 개선하여 더 나은 증발산 추정치를 구하는 데에 도움이 될 것이다. 이 연구에서는 증발 표면을 수직적으로 흙층(soil layer)과 잎층(canopy layer)으로 나눠진 다층 구조로 바라보고, 각 층에서 증발산을 계산하는 방법을 도입했다. 증발 표면을 수직 상에서 구분했기에 각 층의 환경 조건은 그 층을 대표하는 높이에서 관측된 기상자료를 활용할 수 있다. 또한, 식생 활기에 따른 각 층의 복사에너지 유입량과 기공의 여닫힘에 따른 Bowen 비를 통해 식생이 증발산에 미치는 영향을 반영하는 것이 가능하다. 본 연구에서는 Fluxnet에서 제공하는 공분산 방법(eddy covariance method)으로 측정한 자료를 참고하여 다층 구조가 실제 증발산 산정에 타당한가를 논했다. 시스템 내 변화는 주어진 조건에서 엔트로피가 최대로 생성되는 방향으로 발생한다는 Maximum Entropy Production (MEP) 이론을 기반으로 만들어진 증발산 산정법을 통해 각 층의 증발산을 계산했으며, 관측 증발산을 토대로 잎층과 흙층에 유입된 복사에너지의 크기를 비교했다. 결과적으로 잎층에 계산된 복사에너지 흡수능이 낙엽수림의 변화 주기를 잘 반영하는 것을 확인했으며 다층 구조를 도입하는 것이 증발산 산정 향상과 수문-식생 관계를 고려한 증발산 분석에 적절한 접근법임을 보였다.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1263-1270
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• 2010

Sixty patients were divided into three groups. Each group of twenty persons had fed on different diet foods over 5 weeks. Cholesterol had been measured repeatedly five times at an interval of a week during 5 weeks. It resulted from mixed model analysis of repeated measurements data that homogeneous toeplitz covariance matrix pattern was selected as the optimal covariance pattern. The correlations between measurements of different times for the covariance matrix are somewhat highly correlated as 0.64-0.78. Based upon the homogeneous toeplitz covariance pattern model, the time effect was found to be highly significant, but the treatment effect and treatment-time interaction effect were found to be insignificant.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.151-158
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• 2015

This paper discusses how to use projections for the analysis of data from balanced incomplete block designs. A model is suggested as a matrix form for the interblock analysis. A second set of treatment effects can be found by projections from the suggested interblock model. The variance and covariance matrix of two estimated vectors of treatment effects is derived. The uncorrelation of two estimated vectors can be verified from their covaraince structure. The fitting constants method is employed for the calculation of block sum of squares adjusted for treatment effects.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.04a
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• pp.109.2-130
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• 2001

The purpose of this study is (1) to demonstrate that ignoring group differences may mislead to incorrect conclusion when analyzing correlation data (e.g., factor analysis), (2) to highlight the importance of the data analytic method considering group differences, and (3) to provide ways of incorporating group differences in data analysis. In study, 1, ignoring group difference in factor analysis may mislead to incorrect factor structure. To remedy this, z-transform method and group analysis tool in covariance structure models were suggested. In study 2, the group differences effect was illustrated by using real data (IQ test data).

• Survey Research
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• v.2 no.2
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• pp.109-130
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• 2001

The purpose of this study is (1) to demonstrate that ignoring group differences may mislead to incorrect conclusion when analyzing correlation data (e.g.. factor analysis). (2) to highlight the importance of the data analytic method considering group differences. and (3) to provide ways of incorporating group differences in data analysis. In study 1. ignoring group difference in factor analysis may mislead to incorrect factor structure. To remedy this. z-transform method and group analysis tool in covariance structure models were suggested. In study 2. the group differences effect was illustrated by using real data (IQ test data).