• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.145-159
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• 1996

다변량 발병시간자료는 각 개개 환자에게 있어 합병증이 발생되거나 혹은 유사 환자군(집락) 내의 발병시간이 상관되어진 생의학자료에서 흔히 볼 수 있다. HMULCOX는 그런 자료를 분석하기 위한 한글 통계 패키지 가운데 하나이다. 이 프로그램은 관련된 발병시간들이 독립이 아닐때에도 COX 비례 위험 모형의 주변확률분포를 계산해 준다. 주어진 조건으로는 주변확률모형의 기본위험율은 일정한 상수, 흑은 변수라도 관계없다. 또한 치료실패율의 치료변수들(공변량)의 효과에 대해 다양한 통계적 추론이 가능하다. 기본적으로 주변확률분포접근법으로 설계되었지만 HMULCOX는 여러 가지 추론 방법을 선택하는 데 일반적으로 충분하다. 이 프로그램으로 2개의 예를 들어 실행하겠다.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.541-559
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• 2015

VaR is now widely used as an important tool to evaluate and manage financial risks. In particular, it is important to select an appropriate volatility model for the rate of return of financial assets. In this study, both univariate and multivariate models are considered to evaluate VaR of the portfolio composed of KOSPI, Hang-Seng, Nikkei indexes, and their performances are compared through back testing techniques. Overall, multivariate models are shown to be more appropriate than univariate models to estimate the portfolio VaR, in particular DCC and ADCC models are shown to be more superior than others.

• Journal of Wetlands Research
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• v.21 no.4
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• pp.281-289
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• 2019

In this study, the typhoon damage forecasting model was developed for southern inland district. The typhoon damage in the inland district is caused by heavy rain and strong winds, variables are many and varied, but the damage data of the inland district are not enough to develop the model. The hydrological data related to the typhoon damage were hour maximum rainfall amount which is accumulated 3 hour interval, the total rainfall amount, the 1-5 day anticipated rainfall amount, the maximum wind speed and the typhoon center pressure at latitude 33° near the Jeju island. The Multivariate Analysis such as cluster Analysis considering the lack of damage data and principal component analysis removing multi-collinearity of rainfall data are adopted for the damage forecasting model. As a result of applying the developed model, typhoon damage estimated and observed values were up to 2.2 times. this is caused it is difficult to estimate the damage caused by strong winds and it is assumed that the local rainfall characteristics are not considered properly measured by 69 ASOS.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.13-21
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• 2011

This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.297-308
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• 2020

We focus on the functional autoregressive conditional heteroscedasticity (fARCH) modelling to analyze intraday volatilities based on high frequency financial time series. Multivariate volatility models are investigated to approximate fARCH(1). A formula of multi-step ahead volatilities for fARCH(1) model is derived. As an application, in implementing fARCH(1), a choice of appropriate time interval for the intraday return is discussed. High frequency KOSPI data analysis is conducted to illustrate the main contributions of the article.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.91-106
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• 2014

This study tries to cluster the 795 standard watersheds of Korea Water Resources Unit Map using multivariate statistical analysis technique. The 30 factors of watershed characteristics related to topography, stream, meteorology, soil, land cover and hydrology were selected for comprehensive analysis. From the factor analysis, 16 representative factors were selected. The significant factors in order were the pedological feature, scale and geological location and meteorological and hydrological features of the watershed. As a next step, the 73 gauged watersheds were selected for cluster analysis. They are scattered properly to the whole country and the discharge data were within a confidential level. Based on the 73 watersheds, the other ungaged watersheds were clustered by applying the 16 factors and calculating Euclidian distances. The clustering results showed that the similarity between standard watersheds within the same river basin were 87%, 69%, 41%, 52%, and 27% for Han, Nakdong, Geum, Seomjin, and Yeongsan river basins respectively.

• Geophysics and Geophysical Exploration
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• v.1 no.3
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• pp.170-175
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• 1998

A systematic methodology is developed for the prediction of the lithology using electrofacies classification from wireline log data. Multivariate statistical techniques are adopted to segment well log measurements and group the segments into electrofacies types. To consider corresponding contribution of each log and reduce the computational dimension, multivariate logs are transformed into a single variable through principal components analysis. Resultant principal components logs are segmented using the statistical zonation method to enhance the quality and efficiency of the interpreted results. Hierarchical cluster analysis is then used to group the segments into electrofacies. Optimal number of groups is determined on the basis of the ratio of within-group variance to total variance and core data. This technique is applied to the wells in the Korea Continental Shelf. The results of field application demonstrate that the prediction of lithology based on the electrofacies classification works well with reliability to the core and cutting data. This methodology for electrofacies determination can be used to define reservoir characterization which is helpful to the reservoir management.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.395-406
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• 2022

This paper studies the outlier detection method for multivariate long memory time series. The existing outlier detection methods are based on a short memory VARMA model, so they are not suitable for multivariate long memory time series. It is because higher order of autoregressive model is necessary to account for long memory, however, it can also induce estimation instability as the number of parameter increases. To resolve this issue, we propose outlier detection methods based on the VHAR structure. We also adapt the robust estimation method to estimate VHAR coefficients more efficiently. Our simulation results show that our proposed method performs well in detecting outliers in multivariate long memory time series. Empirical analysis with stock index shows RVHAR model finds additional outliers that existing model does not detect.

• The Korean Journal of Financial Management
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• v.18 no.2
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• pp.125-144
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• 2001

본 연구는 선행연구들과 달리 경제변수로 설명할 수 없는 경제주체들의 심리적 요소가 주가에 영향을 미칠 수 있다는 관점에서 주가와 거시경제변수 및 경제주체들의 기대심리간의 장기 균형 및 동학구조관계를 분석한다. 주가는 기업의 내재가치를 나타내며 이는 상당부분 현재와 미래의 경제상황에 의해 영향을 받을 것이다. 미래경제상황을 정확히 예측할 수는 없으나 경제 주체들은 미래경제상황을 예측하게 되며 그 예측은 주가에 반영될 수 있다. 검증결과 BSI 전망치와 같은 경제주체들의 기대심리가 주가결정에 가장 중요한 단일 변수인 것으로 나타났다. 이변량 공적분검증을 실시한 결과 실질주가지수는 BSI와 장기균형관계에 있는 반면 다른 거시경제변수와는 공적분관계에 있지 않은 것으로 나타났다. 다변량 공적분분석에서도 BSI가 포함된 경우에만 KOSPI/P와 장기균형관계에 있는 것으로 나타났다. 벡터오차수정모형으로 동태적 관계를 분석한 결과, 이변량과 다변량 분석 모두에서 이들 두 변수의 오차수정항이 통계적으로 유의하여 장기균형으로부터 이탈에 대하여 상호 조정하는 것으로 나타났다.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2002.10a
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• pp.31-42
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• 2002

최근 지하철 터널은 사용자 편의성, 도심지 접근성 및 원활한 교통처리 등을 고려하여 지반조건이 불리한 상황에서도 터널로 계획되는 경우가 많아지고 있다 따라서 시공중의 터널안정성확보, 굴착에 따른 인접구조물의 침하영향, 발파진동영향 등을 종합적으로 고려한 지보패턴 및 보조·보강공법의 결정이 매우 중요하나 정량적인 판단기준의 부재로 인하여 주로 경험적인 설계에 의존하는 경우가 많다. 본 연구에서는 도심지 지하철 터널의 복합적인 거동특성을 고려하기 위하여 여러 가지 예상위험요소의 정량화 방안을 제안하고, 다변량 통계분석기법을 활용하여 여러 가지 위험 요소들의 특성을 함축적으로 나타내는 소수의 총합적인 지표(안정성인자, 환경성인자)로 대표화 할 수 있음을 검증하였다. 안정성 인자 및 환경성 인자를 이용한 서울시 지하철 00공구 설계사례를 통해 정량화지표(Multiple Index)의 터널설계에의 적용성을 평가하고 이의 설계시 활용방안을 제안하고자 하였다.