• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.158-158
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• 2016

본 연구에서는 극치 분포의 오른쪽 꼬리 부분 예측 시 안정적인 확률수문량 산정하는 확률분포형과 매개변수 추정 방법을 평가하기 위해 Monte Carlo 모의를 수행하였다. 수문자료의 빈도해석에 적합한 것으로 알려진 generalized extreme value (GEV), Gumbel (GUM), generalized logistic (GLO), gamma3 (GAM3), normal (NOR), log-normal3 (LN3) 총 6개의 확률분포형을 바탕으로 오른쪽 꼬리 부분의 확률수문량 추정 성능을 모의 실험을 통해 평가하고자 한다. 30년 이상 자료를 보유한 기상청 지점의 지속기간별 연최대값 자료를 분석한 결과를 바탕으로 모분포를 GEV분포로 선정하였으며 평균이 1.0, 표준편차 0.5, 왜곡도 계수는 0.5, 1.0, 2.0, 3.0, 4.0이 되도록 가정하였다. 또한 자료 길이에 따른 성능 평가를 위해 표본 크기 20, 50, 100, 150, 200개에 대해 분석을 수행하였다. 위와 같은 가정으로 총 25종류(왜곡도계수 5개 ${\times}$ 표본 크기 5개)의 발생된 모분포에 6가지의 확률분포형과 3가지의 매개변수 추정방법(모멘트법, 최우도법, 확률가중모멘트법)을 조합한 18가지의 모델을 비교 분석해보았다. 평가방법으로는 평균 제곱근 오차(Root Mean Square Error, RMSE), 편의(bias), 평균 상대오차(Mean Relative Difference, MRD), 평균 절대 상대오차(Mean Absolute Relative Difference, MARD)를 사용하여 적용 모델의 성능을 비교 분석하였다.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.659-668
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• 2006

The uncertainty of the risk of failure of hydraulic structures can be determined by estimating the variance of the risk of failure based on the methods of moments, probability weighted moments, and maximum likelihood assuming that the underlying model is the Gumbel distribution. In this paper, the variance of the risk of failure was derived. Monte Carlo simulation was peformed to verify the characteristics of the derived formulas for various sample size, design life, nonexceedance probability, and variation coefficient. As the results, PWM showed the smallest relative bias and root mean square error than the others while ML showed the smallest ones for relatively large sample siBes regardless of design life and nonexceedance probability. Also, it was found that variation coefficient does not effect on the relative bias and relative root mean square error.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.13-18
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• 2002

본 논문에서는 급변하는 농촌의 환경을 충분히 반영할 수 있도록 1997년도에 설계되어 사용되고 있는 현행의 농가경제조사를 개선하였다. 새로운 표본 조사구를 선정하기 위하여 1999년도와 2000년도 농가경제조사 조사데이터와 2000년에 실시된 농어업총조사 결과를 심도 있게 분석하였다. 이를 기초로 현재의 농촌 실정에 적합한 조사모집단을 새롭게 구성하였고, 현재의 농촌 환경을 반영할 수 있는 층화 기준을 마련하여 표본 조사구를 추출하였다. 또한, 논벼를 비롯한 6개 주요작물들에 대한 농산물생산비조사의 정도(精度) 향상을 위해서 각 작물별 주산지를 표본 조사구로 선정하였다.

• Survey Research
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• v.11 no.1
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• pp.123-144
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• 2010

In new sample design for Korea Housing Survey to research about housing policy, total strata are forty five because individual results of sixteen regions are estimated. The sample size is determined by sample errors of several variables which are the living area, family income, householder income, and living expenses. The sample size of each region is determined by relative standard error of existing result, and the strata sample size is to use the square root proportion allocation. Enumeration districts are sampled by the probability proportion to size systematic sampling in proportion to the enumeration district size, and the systemic sampling to use assortment characteristics. We considered a new apartment complex because of variation reflections which are rebuilder and redevelopment of houses. To get estimators of mean and variance, we used the design weighting, non-response adjusting, and post-stratification. In order to consider estimation efficiency, we calculate the design effect using estimators of variance.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.399-400
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• 2017

홍수나 가뭄 등 극치 현상의 통계분석 및 빈도해석에 있어 극치분포형이 널리 사용되고 있으며, 이러한 극치분포형의 특성을 이해하기 위해서는 분포형의 오른쪽 꼬리(right tail) 부분 특성을 자세히 분석할 필요가 있다. 이에 따라 본 연구에서는 Monte Carlo 모의를 통하여 다양한 극치분포형의 오른쪽 꼬리 부분의 통계적 특성 및 그 예측 능력을 연구하였다. 극치분포형으로는 우리나라 확률수문량 산정에 널리 활용되고 있는 generalized extreme value (GEV), Gumbel, generalized logistic 분포를 사용하였으며, 매개변수 산정 방법으로는 확률가중모멘트법을 사용하였다. 모의실험의 모분포로는 수문빈도해석에서 많이 사용되는 GEV 분포를 사용하였고, 30년 이상 자료를 보유한 기상청 지점 자료의 왜곡도를 조사하여 모의실험에 사용되는 모집단의 왜곡도로 가정하여 표본 자료를 발생시켰다. 예측 능력의 평가는 재현기간 10~1000년의 확률수문량을 왜곡도계수를 고려한 GEV 도시위치공식을 이용하여 GEV 확률지에 도시하고, 평균제곱근오차(root mean square error), 편의(bias), 평균상대오차(mean relative difference), 평균절대상대오차(mean absolute relative difference)를 이용하여 최적 분포형을 선정함으로써 이루어진다. 또한 예측 능력 평가결과의 타당성 확인을 위해 극치분포형의 적합정도를 잘 나타낸다고 알려진 modified Anderson-Darling 방법의 검정결과와 비교하여 적절성을 확인하였다.

• Survey Research
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• v.5 no.1
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• pp.103-130
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• 2004

Main monthly household surveys conducted by Korea National Statistical Office are economically active population survey(EAPS) and household income and expenditure survey(HIES). Samples of these two surveys are redesigned every 5 years based on Census. This paper is about sample redesign of household survey conducted in 2002 based on 2000 Census. Main improvements of 2002 sample redesign are the introduction of rotation sampling system, the expansion of HIES survey area from urban to whole country and the foundation of basement to make small area estimation for the unemployment statistics. Also the number of sample households within a enumeration district(ED) is reduced from 24 to 20. That makes it possible to select more ED samples which provides better precision for EAPS and HIES. To select representative samples for the population, different classification index is used for each metropolitan area and provinces.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.605-617
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• 2014

Small area estimation is a statistical inference method to overcome large variance due to a small sample size allocated in a small area. A shrinkage estimator obtained by minimizing relative error(RE) instead of MSE has been suggested. The estimator takes advantage of good interpretation when the data range is large. A semiparametric estimator is also studied for small area estimation. In this study, we suggest a semiparametric shrinkage small area estimator and compare small area estimators using labor statistics.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.263-274
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• 2010

Business survey index(BSI) is an economic forecasting index made on the basis of the past achievement of the company and enterpriser's plan and decision for the future. Even the index is very popular in economic situations, only a little research result is known to the public. In the paper we investigate statistical properties of BSI. We define population BSI in the finite population and estimate it unbiasedly. Also we derive the variance of the estimated BSI and its unbiased estimator. In addition, confidence interval of the estimated BSI is proposed. We asserte that confidence interval of the estimated BSI is more reasonable than the relative standard error.

• Journal of Korea Foresty Energy
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• v.17 no.1
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• pp.23-29
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• 1998

This study was carried out to develop the regression model for estimating biomass of natural Pinus densiflora forests by stand density in northeast Chinese area of Mt. Paekdu. Four allometric regression models(W=aD$^b$, W=a(D$^2$H)$^b$. logW=a+b$\cdot$ logD+cD and logW=a+b$\cdot$log(D$^2$H)+c(D$^2$H)) were used to estimate biomass for each of the tree components. The suitable regression model for estimating biomass of stem, bark and whole tree above ground was logW=a+b$\cdot$log(D$^2$H)+c(D$^2$H), and that for biomass of branch, needle and needle area, logW=a+b$\cdot$logD+cD for all of the stand density classes.