• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.123-138
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• 2010

After selecting a group of women with premenstrual syndrome based on daily distress scores of 28 days, one needs to estimate threshold for the change of symptoms, which would be useful for the clinician's diagnosis in hospitals. However, a test of whether a change has occurred has to precede the estimation of the threshold. In this paper, we apply parametric and nonparametric testing methods to an example data obtained from a group of women. Nonparametric method does not assume any distributional form of distress scores and parametric testing method is based on the normal distributions of linear regression lines. Therefore, the optimal situation of both methods would be different and we will assess it with a simulation study.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.181-194
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• 2017

This study aims to develop a regression model for forest volume estimation using field-collected forest inventory information and airborne LiDAR data. The response variable of the model is forest stem volume, was measured by random sampling from each individual plot of the 30 circular sample plots collected in Bonghwa-gun, Gyeong sangbuk-do, while the predictor variables for the model are Height Percentiles(HP) and Height Bin(HB), which are metrics extracted from raw LiDAR data. In order to find the most appropriate model, the candidate models are constructed from simple linear regression, quadratic polynomial regression and multiple regression analysis and the cross-validation tests were conducted for verification purposes. As a result, $R^2$ of the multiple regression models of $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}$ among the estimated models was the highest at 0.509, and the PRESS statistic of the simple linear regression model of $HP_{25}$ was the lowest at 122.352. $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}-based$ models, thus, are comparatively considered more appropriate for Korean forests with complicated vertical structures.

• Journal of Digital Convergence
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• v.12 no.9
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• pp.111-121
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• 2014

This study is designed to predict the total ad expenditure of Korea, and six media ad expenditures in 5 years based on the past 20 years ad expenditure date. We use annual data published by Cheil Worldwide advertising data analysis. Time series, SUR method, exponential smoothing method and regression analysis were used for exploring the data. The results showed that the total advertising expenditure in 2018 is predicted to 10,873 billion wons. On the basis of the findings, implications are discussed for academicians as well as practitioners.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.594-600
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• 2017

The new design methodology of prediction model and pattern classification, which is based on the dimension reduction algorithm called principal component analysis, is introduced in this paper. Principal component analysis is one of dimension reduction techniques which are used to reduce the dimension of the input space and extract some good features from the original input variables. The extracted input variables are applied to the prediction model and pattern classifier as the input variables. The introduced prediction model and pattern classifier are based on the very simple regression which is the key point of the paper. The structural simplicity of the prediction model and pattern classifier leads to reducing the over-fitting problem. In order to validate the proposed prediction model and pattern classifier, several machine learning data sets are used.

• Composites Research
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• v.14 no.3
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• pp.69-76
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• 2001

This paper proposes a double cantilever sandwich-beam method fur evaluating the frequency dependence of dynamic characteristics of rubbers. The flexural vibration of a double cantilever sandwich-beam specimen with an inserted rubber layer was studied using a finite element simulation in combination with the sine-sweep test. Quadratic relationships of dynamic elastic modulus and material loss factor of rubbers with frequency were suggested employing the least square error method.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.427-439
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• 2017

Quade (1967) proposed RANK ANCOVA, which is a nonparametric method to test differences between treatments when there are covariates. Hwang and Kim (2012) also proposed a joint placement test on covariate-adjusted residuals. In this paper, we proposed a new nonparametric method to control the effect of covariate on a response variable that uses linear statistics on covariate adjusted-residuals. The score function used in the linear statistics was proposed by Jeon and Kim (2016). Monte Carlo simulation is also conducted to compare the empirical powers of the proposed method with previous methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.513-513
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• 2022

국가물관리기본법에 의거하여 통합물관리 정책에 부합하는 농어촌용수 계획 및 관리 요구에 따른 유역 및 용수구역 단위의 물관리 필요하며, 국가수자원계획의 물수급 정책 수립시 농업용수의 공급, 이용 및 관리 특성 고려되어야 한다. 현재 농업용수는 개수로 방식 용수공급체계 및 수문 직접조작에 의한 용수배분체계로 공급량 대비 사용량(벼의 생육에 사용된 수량)의 비율이 48%에 불과하고, 농경지 상류와 하류의 공급량 차이가 크게 발생하며, 경지면적 감소가 공급 필요량 감소로 연결되지 않는다. 현재 국가유역수자원모델 (K-WEAP, K-MODSIM)은 모델이 가진 분석 능력의 한계로 인하여 농업용수 물수급 해석에 왜곡이 발생하기 때문에, 농업용수 특성이 반영된 농업용수 수요·공급 표준화 모형이 필요하다. 본 연구에서는 기존 유역물수지모델 현황 및 농업용수 적용의 한계점을 파악하고, 농업용수의 공급 및 이용을 고려한 유역 물수지 모형 개발을 목표로 한다. 기존 농업용수 물수지 분석은 순물소모량 개념 적용에 따른 회귀수량 획일화와 이에 따른 공급량 왜곡, 유역내 복잡하고 다양한 농업용수 공급체계를 하나의 가상저수지로 단순화 함으로서 유역내 들녘별 농업용수 과부족 분석 불가능, 하천과 저수지 공급 우선순위 현장과 불일치, 노후된 기초자료 등의 한계가 존재하며, 이를 위한 개선방안을 도출하고자 한다. 또한, 농업용수 회귀수량의 경우 실측기반의 회귀수량 산정 방법을 제시하고자 하며, 단일 수원공 및 복합 수원공의농업용수 물수지 분석 방법을 개발하고자 한다. 본 연구의 목적은 농업용수 물수급 특성이 국가수자원계획에 반영할 수 있도록 기본 수자원모델(K-MODSIM)과 연계가능한 농업용수 표준 모형개발로써, 향후 국가수자원계획(국가물관리기본계획, 전국하천유역수자원관리계획, 농어촌용수이용 합리화계획 등) 수립에 반영될 수 있을 것으로 판단된다.

• International Area Studies Review
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• v.13 no.2
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• pp.89-115
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• 2009

By measuring the consumption risk sharing for the EU regions, we evaluate the performance of various risk sharing channels for the EU. We identify which countries are likely to form the highest risk sharing group among the EU regions by using the DFFITS and DFBETAS diagnostics derived in a statistical regression. Our finding suggests that most western European countries seem to display homogeneous degree of risk sharing. In addition, our result confirms that high risk sharing regions as well as low risk sharing regions are mainly located in many eastern European countries that joined the EU later than western European countries, and implies that the EU members are still dichotomized at large in terms of consumption risk sharing.

• Journal of the Korean association of regional geographers
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• v.14 no.6
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• pp.736-751
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• 2008

Although a growing number of studies have commonly used a simple areal weighting interpolation method to quantify demographic characteristics of impacted areas in environmental equity analysis, the results obtained are inevitably imprecise because of the method's unrealistic assumption that population is evenly distributed within a census enumeration unit. Two alternative areal interpolation methods such as intelligent areal weighting and regression methods can account for the distributional biases in the estimation of impacted populations by making use of additional information about the geographic distribution of population. This research explores five areal interpolation methods for estimating the population characteristics of impacted areas in environmental equity analysis and evaluates the sensitivity of the outcomes of environmental equity analysis to areal interpolation methods. This study used GIS techniques to allow areal interpolation to be informed by the distribution of land cover types, as inferred from a satellite image. in both the source and target units. Independent samples t-test statistics were measured to verify the environmental equity hypothesis while coefficients of variation were calculated to compare the relative variability and consistency in the socioeconomic characteristics of populations at risk over different areal interpolation methods. Results show that the outcomes of environmental equity analysis in the study area are not sensitive to the areal interpolation methods used in estimating affected populations, but the population estimates within the impacted areas are largely variable as different areal interpolation methods are used. This implies that the use of different areal interpolation methods may to some degree alter the statistical results of environmental equity analysis.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.05a
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• pp.157-160
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• 2009

자동차는 지난 한 세기의 짧은 역사를 가지고 현재에 이르기까지 급속한 발전을 이루어왔다. 초기의 기능은 단순히 운송수단과 부의 상징이었지만 최근에는 소비자의 감성과 문화적인 흐름을 반영하는 모습이다. 그리하여 소비자의 감성 분석이 자동차 내장의 가장 중요한 부분으로 여기게 되었다. 자동차에 대한 새로운 감성요구를 실현하기 위해서는 인체와 오랜 시간 접촉해 있는 시트 표피재의 분석이 반드시 필요하다. 본 연구에서는 자동차 시트 표피재의 역학적 특성과 감성을 고려한 고급감을 예측하여 고감성 내장 표피재 개발에 기여하고자 한다. 감성용어는 Softness(유연한), Elasticity(탱글탱글한), Volume(풍성한), Stickiness(끈끈한)를 설정하였으며, 이와 대응하는 표피재의 역학적 특성 치를 측정하였다. 감성 평가에서는 현재까지 알려진 가장 확실하고 재현성 있는 측정법인 일대일 비교법을 통해 고급감에 대해 평가하였다. 이를 통하여 역학적 특성 치와 인간의 감성 평가 치와의 회귀 분석을 실시하여 평가 예측을 가능케 하였다. 즉, 자동차 표피재 중 피혁의 4 가지 물리량으로 인간의 감성인 표피재의 고급감을 예측하여 고감성 자동차 시트 표피재의 개발을 위한 평가 모델링을 구축하였다.