• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.131-143
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• 1994

Under the asymmetric losses (entropy loss and Stein loss), we find the classes of Bayes and empiricla Bayes estimates for estimating the Poisson means when the distributin of means are believed a priori. Following the idea of Efron and Morris (1973), we have a computer simulation to compute a relative savings loss of proposed estimates as compared to the classical estimates.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.763-774
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• 2020

Wind data forecasted from the numerical weather prediction (NWP) model is generally used as the first-guess of the target tracking process to obtain the atmospheric motion vectors(AMVs) because it increases tracking accuracy and reduce computational time. However, there is a contradiction that the NWP model used as the first-guess is used again as the reference in the AMVs verification process. To overcome this problem, model-independent first guesses are required. In this study, we propose the AMVs derivation from Lucas and Kanade optical flow method and then using it as the first guess. To retrieve AMVs, Himawari-8/AHI geostationary satellite level-1B data were used at 00, 06, 12, and 18 UTC from August 19 to September 5, 2015. To evaluate the impact of applying the optical flow method on the AMV derivation, cross-validation has been conducted in three ways as follows. (1) Without the first-guess, (2) NWP (KMA/UM) forecasted wind as the first-guess, and (3) Optical flow method based wind as the first-guess. As the results of verification using ECMWF ERA-Interim reanalysis data, the highest precision (RMSVD: 5.296-5.804 ms^-1) was obtained using optical flow based winds as the first-guess. In addition, the computation speed for AMVs derivation was the slowest without the first-guess test, but the other two had similar performance. Thus, applying the optical flow method in the target tracking process of AMVs algorithm, this study showed that the optical flow method is very effective as a first guess for model-independent AMVs derivation.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.564-571
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• 2007

One of the methods for integrating XML data in distributed environments is using XML view. User can query toward distributed local XML views by using global XQuery queries in XQuery which is a standard query language for searching XML data. The global XQuery queries naturally contain join operations because of integrating and searching distributed heterogeneous data. Since join operations are generally expensive for processing a query, its processing technique is very important for efficient processing of global XQuery queries. Therefore there are some studies on the efficient processing of join operations and one of these studies is that selects minimum join cost by estimating a join selectivity. In case of SQL, there are already some researches for estimating a join selectivity and join cost of global SQL queries. However we can not apply their methods for estimating the selectivity of join operations in SQL queries into XQuery queries because of the structural difference between relational data and XML data. Therefore this paper proposes a method for estimating a selectivity of join operations in XQuery queries using the information of XML views. Our contribution is three threefold. First, we define the difference point for estimating join selectivity between SQL and XQuery. Second, we estimate join selectivity in XQuery queries by referring XML views. Third, we evaluate our estimating method.

• The Korean Journal of Financial Management
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• v.22 no.1
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• pp.119-146
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• 2005

This study applies extreme value theory to get extreme value-VAR for Korean Stock market and showed the usefulness of the approach. Block maxima model and POT model were used as extreme value models and tested which model was more appropriate through back testing. It was shown that the block maxima model was unstable as the variation of the estimate was very large depending on the confidence level and the magnitude of the estimates depended largely on the block size. This shows that block maxima model was not appropriate for Korean Stock market. On the other hand POT model was relatively stable even though extreme value VAR depended on the selection of the critical value. Back test also showed VAR showed a better result than delta VAR above 97.5% confidence level. POT model performs better the higher the confidence level, which suggests that POT model is useful as a risk management tool especially for VAR estimates with a confidence level higher than 99%. This study picks up the right tail and left tail of the return distribution and estimates the EVT-VAR for each, which reflects the asymmetry of the return distribution of the Korean Stock market.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.6-6
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• 2017

본 연구의 목적은 독립적으로 수행되어진 하천 복원 가치추정 선행연구들을 대상으로 메타회귀분석을 활용한 편익이전을 제시함으로써, 편익이전의 타당성 및 적용 가능성을 검토하는 데 있다. 문헌검색을 통해 '하천 가치평가', '하천 가치', '물 환경 가치추정', '하천 편익' 등에 관한 자료를 수집하였으며, 총 60편의 연구를 수집했다. 그 중 중복된 연구를 제외하고 가치추정 단위를 '원/년/가구'로 통일함으로써 51편의 연구를 분석에 사용했으며, 90개의 가치 추정치가 실증분석에 사용되었다. 본 연구는 국내에서 수행된 하천 복원 가치 추정연구를 집대성하여 DB를 구축하고 요약통계량을 중심으로 선행연구 결과를 기술하였으며, 메타회귀분석을 실시한 후, policy site의 특성과 조건에 맞게 함수를 조정하고, 조정된 함수를 사용하여 policy site의 가치를 예측하였다. 종속변수로는 총 가치(원/년/가구, 2015년 불변가격)가, 독립변수로는 하천유형, 위치, 규모, 환경 서비스특성, 그리고 방법론 특성, 지불형태, 대상지 사회경제적 특성 변수들이 포함되었다. 모형의 추정결과 조정된 값은.420으로써 종속변수 총변이의 42.0%를 모형이 설명하는 것으로 나타났다. 메타회귀분석을 통해 본류에서 멀어지는 소규모하천일수록 하천의 경제적 가치를 더 크게 느끼는 것으로 나타났으며, 전체적인 영향력 크기를 고려해 본다면 하천을 복원할 때 수질정화기능, 서식지기능, 이수기능, 치수기능, 여가 및 수변공간으로의 기능 순으로 고려하는 것이 하천의 가치를 보다 높일 수 있을 것으로 보였다. 또한 지불방법은 매월, 인당 지불하는 것으로 제시할 때 경제적 가치 추정치를 높일 수 있는 것으로 해석되었다. 모델추정 결과를 활용한 함수이전에서는 만경강의 특성을 반영하고 조정함으로써 만경강의 가치를 추정하였으며, 모형으로부터 얻은 만경강 가치 예측치는 가구당 매년 41,214원으로 추정되었다. 본 연구의 메타회귀분석은 선행연구를 객관적으로 종합할 수 있는 분석의 틀로서 충분한 활용 타당성이 인정되는 것으로 보이며, 편익이전 시에 policy site의 자원특성과 조건에 맞춰 함수를 조정하여 예측치를 제시함으로써 메타회귀분석 함수이전의 융통성을 보여주었다. 이에 메타회귀분석을 통한 편익이전은 타당성 및 적용 가능성 측면에서 긍정적으로 판단된다.

• Journal of Advanced Navigation Technology
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• v.16 no.4
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• pp.602-610
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• 2012

Ionospheric delay error, one of main error sources in GPS signal, varies with signal frequency. Dual-frequency user uses L1, L2 frequency pseudorange to estimate the ionospheric delay, and there are errors caused by pseudorange measurement noise. So, filter is usually used to smooth the measurement. Weighted hatch filter can estimate optimal smoothed pseudorange measurement. But measurement noise model is needed to use this filter. In this paper, measurement noise modeling is conducted for NDGPS reference station. Using noise modeling result, weighted hatch filter estimate smoothed pseudorange measurement and ionospheric delay. Standard deviation of ionospheric dealy error drops to one-twenty fifth of non-filtered result.

• The Journal of the Acoustical Society of Korea
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• v.39 no.3
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• pp.163-169
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• 2020

The asynchronous bistatic sonar needs to estimate direct blast arrival time at a receiver to localize targets, and therefore the direct blast arrival time estimation error could be added to target localization error in comparison with synchronous system. Direct blast especially appears as several peaks at the matched filter output by multipath, thus we compared the first peak detection technique and the maximum peak detection technique of those peaks for direct blast arrival time estimation through sea trial data. The test was performed in a shallow sea with bistatic sonar made up of spatially separated source and line array sensors. Line array sensors obtained the target signal which is generated from the echo repeater. As a result, the first peak detection technique is superior to maximum peak detection technique in direct blast arrival time estimation error. The result of this analysis will be used for further research of target tracking in the asynchronous bistatic sonar.

• The Korean Journal of Financial Management
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• v.13 no.1
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• pp.159-184
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• 1996

재무모형의 검증과 포트폴리오의 성과측정 등에 이용할 목적으로 시장모형에 의한 베타계수를 추정할 때 대부분의 연구들은 측정간격을 달리한 수익률의 자료들 즉 일간, 주간 혹은 월간수익률의 자료들 중에서 임의적으로 하나를 선택하고 있다. 그런데 진정한 투자계획기간과 다른 기간간격에 대하여 계산된 수익률자료의 임의적 선택은 시장모형에 의한 베타계수의 추정치에는 물론 그러한 추정치를 이용한 재무모형의 검증 및 포트폴리오의 성과측정 등에 영향을 미칠 수 있다는 점이 몇몇 연구자들에 의하여 지적되고 있다. 본 연구는 기업규모와 베타의 크기에 따라 구성된 포트폴리오를 대상으로 하여 수익률의 측정간격이 시장모형에 의한 베타계수의 추정치에 어떠한 영향을 미치게 되는가를 실증적으로 살펴보고 있다. 1984년 1월 4일부터 1995년 12월 27일까지의 기간(총거래일수 3515일)에 걸쳐 수익률의 측정간격을 달리하여 산출한 연속복리수익률을 이용하여 분석한 결과에 의하면, 규모나 베타가 시장평균에 비하여 상대적으로 작은 포트폴리오에 있어서는 수익률의 측정간격이 길어짐에 따라 추정되는 베타계수는 더욱 커지고, 반면에 규모나 베타가 시장평균에 비하여 상대적으로 큰 포트폴리오에 있어서는 수익률의 측정간격이 길어짐에 따라 베타계수는 더욱 작아진다는 것을 알 수 있다. 그래서 월간수익률과 같이 장기수익률의 자료를 이용할 경우에 시장모형에 의해 추정되는 베타계수는 규모가 작을수록 크게 나타나지만, 일간수익률과 같이 단기수익률의 자료를 이용할 경우에는 규모가 작다(크다)고 해서 추정되는 베타계수가 반드시 크게(작게) 나타나지는 않는다. 그리고 본 연구는 수익률의 측정간격을 달리함에 따라 추정되는 베타계수에 차이가 나타나는 주된 원인이 각 포트폴리오의 단위기간수익률에 있어서 시장지수의 단위기간수익률에 대한 시점간 교차상관의 상대적 강도에 있다는 것을 보여주고 있다.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.43-56
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• 2005

In this study, a robust estimation method for the first-order autocorrelation coefficient in the time series model following AR(l) process with additive outlier(AO) is investigated. We propose the L-type trimmed least squares estimation method using the preliminary estimator (PE) suggested by Rupport and Carroll (1980) in multiple regression model. In addition, using Mallows' weight function in order to down-weight the outlier of X-axis, the bounded-influence PE (BIPE) estimator is obtained and the mean squared error (MSE) performance of various estimators for autocorrelation coefficient are compared using Monte Carlo experiments. From the results of Monte-Carlo study, the efficiency of BIPE(LAD) estimator using the generalized-LAD to preliminary estimator performs well relative to other estimators.

• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.548-555
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• 2013

The localization of sources has a numerous number of applications. To estimate the position of sources, the relative delay between two or more received signals for the direct signal must be determined. Although the generalized cross-correlation method is the most popular technique, an approach based on eigenvalue decomposition (EVD) is also popular one, which utilizes an eigenvector of the minimum eigenvalue. The performance of the eigenvalue decomposition (EVD) based method degrades in the low SNR and the correlated environments, because it is difficult to select a single eigenvector for the minimum eigenvalue. In this paper, we propose a new adaptive algorithm based on Canonical Correlation Analysis (CCA) in order to extend the operation range to the lower SNR and the correlation environments. The proposed algorithm uses the eigenvector corresponding to the maximum eigenvalue in the generalized eigenvalue decomposition (GEVD). The estimated eigenvector contains all the information that we need for time delay estimation. We have performed simulations with uncorrelated and correlated noise for several SNRs, showing that the CCA based algorithm can estimate the time delays more accurately than the adaptive EVD algorithm.