• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.149-160
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• 2015

This study aims to estimate the forest volumes of the upper region of Nam-Han River in ecosystem zoning by forest types and age classes, and to suggest the optimal estimation method through the comparison of the standard errors according to the spatial unit. In the estimation of forest volumes, we used both of direct estimation, which uses sample plots of the target area only, and synthetic estimation, which includes sample plots of the expanded areas as well as those of the target area. As for the spatial expansion, we applied four standards for synthetic estimator: Mountainous zone, Neighbor ecosystem region, Gangwon province, and Buffer zone. The results show that average forest volume per ha, calculated by direct estimation, was $143.5m^3/ha$, while that by synthetic estimation with each standard, was estimated at $146.9m^3/ha$ by Gangwon province, $144.8m^3/ha$ by Buffer zone, $139.8m^3/ha$ by Neighbor ecosystem region, and $138.6m^3/ha$ by Mountainous zone, respectively. The standard errors of direct estimation was $1.79m^3/ha$, while those of synthetic estimation showed not a great difference among the errors. Meanwhile, considering the standard errors by forest type, the lowest was ${\pm}2.3m^3/ha$ of broad-leaved forest, followed by ${\pm}3.3m^3/ha$ of mixed forest, and ${\pm}4.8m^3/ha$ of coniferous forest.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.463-470
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• 2016

In analysing a time series on the frequency domain, the spectral estimator (or periodogram) is a very useful statistic to identify the periods of a time series. However, the spectral estimator is very sensitive in nature to outliers, so that the spectral estimator in terms of M-estimation has been studied by some researchers. Pak (2001) proposed an empirical method to choose a tuning parameter for the Huber's M-estimating function. In this article, we try to implement Pak's estimation proposal in the spectral estimator. We use the Korean housing price index as an example data set for comparing various M-estimating results.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.211-213
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• 2001

히스토그램은 질의 최적화글 위해 사용되는 튿-계 정또 중 하나이다. 최근에는 방대한 데이타에 대한 범위 질의의 선택도 추정 방법의 하나로 사용되기도 한다. 히스토그램을 통한 범위 질의의 선택도 추정 결과는 항상 오차를 포함한다. 따라서 결과의 신뢰성을 보장하기 위해 선택도에 대한 오차를 추정하는 방법이 요구된다. 추정된 선택도의 오차 추정에 대한 기존 방법은 1차원 히스토그램만을 고려하여 하나의 애트리뷰트의 값에 따라 빈도의 분포를 반영하므로 애트리뷰트가 많은 다차원 히스토그램에 바로 적용시키는데 문제가 있다. 이 논문에서는 기존의 추정된 선택도에 대한 오차 추정 기법들을 다차원에 적용할 수 있게 확장한 M-Max, M-Sum 기법을 제안하고, 두 기법을 합친 하이브리드 기법을 제안한다. 실험을 통해 M-Sum 기법과 하이브리드 기법이 M-Max 기법보다 정확한 오차 추정 기법임을 보이고, 또한 작은 기억 공간에서도 두 기법이 오차를 보다 정확하게 추정함을 보인다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1139-1150
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• 2014

This study modified the Parameter-elevation Regression on Independent Slopes Model (PRISM) and investigated the applicability of the modified model (M-PRISM) in estimating $1km{\times}1km$ gridded daily precipitation over South Korea. The model parameters of M-PRISM were estimated by regression curves and were validated using the Jackknife method at the Korean Meteorological Administration (KMA) stations. The results indicate that M-RPISM shows better performance in estimating the frequency of daily precipitation than PRISM while M-PRISM has similar performance to PRISM in estimating the daily precipitation amount. Thus the M-PRISM model proposed in this study can be very useful to estimate high resolution daily precipitation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.6
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• pp.1511-1519
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• 2010

In this paper, we proposed and analyzed a RSSI based cooperative localization algorithm considering wireless propagation characteristics in indoor and outdoor environments for wireless sensor networks, which can estimate the BN position. The conventional RSSI based estimation scheme has low precision ranging due to instability propagation characteristics by time variable. Hence, we implemented ray-launching simulator for analysis of propagation characteristics in 4 case, and experimented proposed localization scheme with 4 RN and 1 to 5 BN. Simulation results show that NLCA has estimation error as 2m-3.5m, however, proposed CLA/ECLA has 1.3m-2.5m/0.5m-1.2m by same environments. Therefore, if we can consider channel characteristics, the proposed algorithm provides higher localization accuracy than RSSI based conventional one.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.293-300
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• 1997

재하강 M-추정함수는 여러 가지 장점을 갖고 있는 반면 이를 이용하여 추정치를 구할 경우 수치 해석상의 문제점을 갖고 있다. 물론, 그러한 문제들은 인위적인 방법에 의해 해결될 수 있으나 본 논문에서는 보다 근본적으로 문제를 해결하려 한다. 그 방법으로써 재하강 함수의 문제점을 갖고 있지 않은 Huber 형태에 유사하고 재하강 함수의 장점도 갖고 있는 새로운 형태의 재하강 M-추정함수를 유도하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1443-1454
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• 2014

In this study, we assess impact of spatial resolution of radar rainfall and a distributed hydrologic model on parameter estimation and rainfall-runoff response. Radar data measured by S-band polarimetric radar located at Mt. Bisl in the year of 2012 are used for the comparative study. As different rainfall estimates such as R-KDP, R-Z, and R-ZDR show good agreement with ground rainfall, R-KDP are applied for rainfall-runoff modeling due to relatively high accuracy in terms of catchment averaged and gauging point rainfall. GRM (grid based rainfall-runoff model) is implemented for flood simulations at the Geumho River catchment with spatial resolutions of 200m, 500m, and 1000m. Automatic calibration is performed by PEST (model independent parameter estimation tool) to find suitable parameters for each spatial resolution. For 200m resolution, multipliers of overlandflow and soil hydraulic conductivity are estimated within stable ranges, while high variations are found from results for 500m and 1000m resolution. No tendency is found in the estimated initial soil moisture. When parameters estimated for different spatial resolution are applied for other resolutions, 200m resolution model shows higher sensitivity compared to 1000m resolution model.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.219-228
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• 2004

In this paper we construct prediction intervals for nonlinear time series models using the bootstrap. We compare these prediction intervals to traditional asymptotic prediction intervals using quasi-score estimation function and M-quasi-score estimating function comprising bounded functions. Simulation results show that the bootstrap method leads to improved accuracy. The accuracy of the bootstrap is empirically demonstrated with the consumer price index.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.3
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• pp.127-143
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• 2016

This study explored the variability in the accuracy of dasymetric population estimation with different grid cell sizes. Dasymetric population maps for Fulton County, Georgia in the US were generated from 30m to 420m at intervals of 30m using an automated intelligent dasymetric mapping technique, population data, and original and simulated land use and cover data. The accuracies of dasymetric population maps were evaluated using RMSE and adjusted RMSE statistics. Lumped fractal dimension values were calculated for the dasymetric population maps generated from resolutions of 30m to 420m using the triangular prism surface area (TPSA) method. The results show that a grid cell size of 210m or smaller is required to estimate population more accurately in terms of thematic accuracy, but a grid cell size of 30m is required to meet an acceptable spatial accuracy of dasymetric population estimation in the study area. The fractal analysis also indicates that a grid cell size of 120m is the optimal resolution for dasymetric population estimation in the study area.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.265-275
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• 1993

This paper deals with the M-estimators in regression model. The class of M-estimators is defined on nonlinear regression model and the conditions to hold the consistency of the considered estimators are suggested when the parameter space of the model is compact.