• Journal of Korean Society for Atmospheric Environment
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• v.19 no.1
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• pp.93-100
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• 2003

In this technical information, the long-term measurement data at Gosan between 1992 and 2001 are analyzed with various statistical methods. First. it was confirmed that the basic assumption of t-test is important to classify data correctly. Second, it was founded that the difference of the number of data per month can affect the averaged concentration. Third, by using a non-parametric statistical method long term trend of aerosol composition free from seasonal effects is obtained.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.2
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• pp.140-148
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• 2007

The remote sensing observations of land surface properties are inevitably influenced by the landscape heterogeneity. In this paper, we introduce a geostatistical technique to provide a quantitative interpretation of landscape heterogeneity in terms of key land surface parameters. The study areas consist of the two KoFlux sites: (1) the Gwangneung site, covered with temperate mixed forests on a complex terrain, and (2) the Haenam site with mixed croplands on a relatively flat terrain. The semivariogram and fractal analyses were performed for both sites to characterize the spatial heterogeneity of two radiation parameters, i.e., land surface temperature (LST) and albedo. These parameters are the main factors affecting the reflected longwave and shortwave radiation components from the two study sites. We derived them from the high-resolution Landsat ETM+ satellite images collected on 23 Sep. 2001 and 14 Feb. 2002. The results of our analysis show that the characteristic scales of albedo was >1 km at the Gwangneung site and approximately 0.3 km at the Haenam site. For LST, the scale of heterogeneity was also >1 km at the Gwangneung site and >0.6 to 1.0 km at the Haenam site. At both sites, there was little change in the characteristic scales of the two parameters between the two different seasons.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.437-445
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• 2000

The present paper is introducing a new model so called TAR-GARCH in the context of stock price analysis Conventional models such as AR(l), TAR(l), ARCH(I) and GARCH( 1,1) are briefly reviewed and TAR-GARCH is suggested in analyizing domestic stock prices. Also, relevant iterative estimation procedure is developed. It is seen that TAR-GARCH provides the better fit relative to traditional first order models for stock prices data in Korea.

• Journal of Wetlands Research
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• v.20 no.3
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• pp.201-209
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• 2018

In this study, the water quality index was calculated using the water quality monitoring data of the major tributaries of the Nakdong River and long-term trend analysis was performed to identify the tributaries requiring priority management. We used a Real-Time Water Quality Index method implemented by the Ministry of Environment. Linear regression as a parametric method and Mann-Kendall Test and Sen Slope Test as a nonparametric method were applied for the trend analysis. The water quality index of major tributaries except for Migeon2 and Seokyo2 was in the range below Fair grade and there were no significant trends for the rest of the sites except Bukan, Chennae, Hogye, Yongdeok. Therefore, in order to improve the water quality of the main stream, management of the tributaries should be preceded.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1213-1224
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• 2011

In high dimensionality where the number of variables are excessively larger than observations, it is required to remove the noninformative variables to cluster observations. Most model-based approaches for variable selection have been considered under the assumption of homoscedasticity and their models are mainly estimated by a penalized likelihood method. In this paper, a different approach is proposed to remove the noninformative variables effectively and to cluster based on the modified normal mixture model simultaneously. The validity of the model was provided and an EM algorithm was derived to estimate the parameters. Simulation studies and an experiment using real microarray dataset showed the effectiveness of the proposed method.

• Journal of the Korean Society of Clothing and Textiles
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• v.20 no.4
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• pp.586-595
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• 1996

급변하는 국내외의 교역환경 변화에 따라 의류산업에도 새로운 경영 전략 수립이 요구되고 있다. Quick Response(QR)는 의류제품을 생산하는 기업의 경쟁력을 증가시키기 위해 소개된 새로운 경영 전략으로서, 유통 채녈 사이에 정보와 상품의 흐름을 효울화시켜 최대의 소비자 만족을 제공한다. 본 연구의 목적은 QR technologies의 사용현황을 밝히고 기업 특성과 QR technologies 사용수준과의 관계를 조사하였다. 종족변수는 QR technologies의 사용 수준이며, 선별된 독립변수들은 기업크기 (firm size), 기업전략 (organizational strategy), 제품종류(product category) , 패션변화(fashion change), 주기 적변화(seasonal change)였다. 조사대상은 미국 전역에서 무작위로 추출된 306개의 의류업체를 대상으로 하였으며, 1차 우편과 2차 전화로, 설문지를 통해 자료수집을 하였다. 설문 응답률은 47%(n=103)였고, 자료의 분석은 기술통계(i, e., 빈도, 퍼센트)와 비모수통계기법을 사용하였다. 가장 많이 사용되고 있는 QR technologies는 소량주문(small lot orders) , 단기 사이클 재단 계획 (short cycle cut planning)과 고객의 견이 반영 된 생산계획 (Production planning with customers)이였다. 가장 적게 사용되고 있는 QR technologies는 전자 재주문(electronic reorder)과 단위 생산 시스템 (unit production system)이 였다. QR technologies 사용수준에 관계가 있는 것은 기업크기 (firm size), 기업전략 (organizational strategy), 패션변화(fashion change) 임으로 나타났다. 의류업체의 크기가 클수록, 혁신적 선도기업 일수록, 패션변화가 큰 제품을 취급할수록 QR technologies의 사용수준이 높은 것으로 나타났다. 의류업체는 자원과 생산하는 제품종류에 따라 경영전략과 QR technologies의 사용수준이 다양하였다.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.833-842
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• 2013

The quantile mapping is utilized to reproduce reliable GCM(Global Climate Model) data by correct systematic biases included in the original data set. This scheme, in general, projects the Cumulative Distribution Function (CDF) of the underlying data set into the target CDF assuming that parameters of target distribution function is stationary. Therefore, the application of stationary quantile mapping for nonstationary long-term time series data of future precipitation scenario computed by GCM can show biased projection. In this research the Nonstationary Quantile Mapping (NSQM) scheme was suggested for bias correction of nonstationary long-term time series data. The proposed scheme uses the statistical parameters with nonstationary long-term trends. The Gamma distribution was assumed for the object and target probability distribution. As the climate change scenario, the 20C3M(baseline scenario) and SRES A2 scenario (projection scenario) of CGCM3.1/T63 model from CCCma (Canadian Centre for Climate modeling and analysis) were utilized. The precipitation data were collected from 10 rain gauge stations in the Han-river basin. In order to consider seasonal characteristics, the study was performed separately for the flood (June~October) and nonflood (November~May) seasons. The periods for baseline and projection scenario were set as 1973~2000 and 2011~2100, respectively. This study evaluated the performance of NSQM by experimenting various ways of setting parameters of target distribution. The projection scenarios were shown for 3 different periods of FF scenario (Foreseeable Future Scenario, 2011~2040 yr), MF scenario (Mid-term Future Scenario, 2041~2070 yr), LF scenario (Long-term Future Scenario, 2071~2100 yr). The trend test for the annual precipitation projection using NSQM shows 330.1 mm (25.2%), 564.5 mm (43.1%), and 634.3 mm (48.5%) increase for FF, MF, and LF scenarios, respectively. The application of stationary scheme shows overestimated projection for FF scenario and underestimated projection for LF scenario. This problem could be improved by applying nonstationary quantile mapping.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.149-160
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• 2019

The electricity consumption time series data of 'A' University from July 2016 to June 2017 is analyzed via nonparametric functional data clustering since the time series data can be regarded as realization of continuous functions with dependency structure. We use a Bouveyron and Jacques (Advances in Data Analysis and Classification, 5, 4, 281-300, 2011) method based on model-based functional clustering with an FEM algorithm that assumes a Gaussian distribution on functional principal components. Clusterwise analysis is provided with cluster mean functions, densities and cluster profiles.

• The Journal of Society for e-Business Studies
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• v.22 no.1
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• pp.15-35
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• 2017

The method of weighted sum of evaluation using AHP is widely used in feasibility analysis and alternative selection. Final scores are given in forms of weighted sums and the alternative with largest score is selected. With two alternatives, as in feasibility analysis, the final score greater than 0.5 gives the selection but there remains a question that how large is large enough. KDI suggested a concept of 'grey area' where scores are between 0.45 and 0.55 in which decisions are to be made with caution, but it lacks theoretical background. Statistical testing was introduced to answer the question in some studies. It was assumed some kinds of probability distribution, but did not give the validity on them. We examine the various cases of weighted sum of evaluation score and show why the statistical testing has to be introduced. We suggest a non-parametric testing procedure which does not assume a specific distribution. A case study is conducted to analyze the validity of our suggested testing procedure. We conclude our study with remarks on the implication of analysis and the future way of research development.