• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.43-47
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• 2005

확률밀도함수가 명확히 표현되지 않고 오직 백분위함수로만 표현되는 분포에서 최우추정치를 구하는 수치적 최적화 알고리즘에 대해서 연구하였다. 이 최우추정 알고리즘을 수문학 등에서 사용되는 5-모수의 웨이크비 분포에 적용하였으며, 몬테카를로 시뮬레이션을 통하여 L-적률추정법과 그 성능을 비교하였다.

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.113-113
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• 2017

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.402-402
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• 2017

수공구조물 설계 시, 설계홍수량 산정에는 실측 홍수량 자료를 활용한 홍수빈도해석이 필요하다. 그러나 홍수량 자료의 관측연한, 유역변화 등의 신뢰성 문제로 확률강우량을 활용한 빈도홍수량 간접추정방법이 표준화된 실정이다. 문제는 확률강우량 산정에 활용된 확률밀도함수와 그 매개변수에 따른 불확실성이 존재한다는 점이다. 특히 저빈도에서 고빈도로 갈수록 확률밀도함수의 불확실성은 크게 증가하여, 사실상 추정결과에 대한 물리적 의미를 부여하기 어렵다. 이에 본 연구에서는 PMF를 물리적 상한선으로 설정하는 상한분포함수(Upper bounded distribution functions)를 적용하여, 실측 홍수량에 대한 홍수빈도해석 방법을 제안하고자 한다. 검정방법은 먼저, 임의 유역을 대상유역으로 선정하여 홍수빈도해석을 수행하고, 상한분포함수는 EV4, LN4, TDF를 적용한다. 최종적으로 빈도홍수량 간접추정방법과 비교 분석하여, 적용성을 검토하고자 한다. 본 연구결과는 빈도홍수량 간접추정방법에 대한 비교 검토방법에 대한 적절한 대안이 없다는 측면에서 의의를 찾을 수 있으며, 향후 홍수량 자료 신뢰성이 확보되는 시점에서 지역홍수빈도 분석으로 확장할 수 있을 것으로 판단된다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.1
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• pp.1-9
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• 2012

Tidal residual is being an important factor by the influence of the climate change in terms of the coastal safety and defense. It is one of the most important factor for the determination of the reference sea level in order to check the safety and performance of the coastal structures in company with the typhoon intensity variation. The probability density function (pdf) of tidal residuals in the Korean coasts have a non-ignorable skewness and high kurtosis. It is highly restricted to the application of the normal pdf assumption as an approximated pdf of tidal residuals. In this study, the pdf of tidal residuals estimated using the Kernel function is suggested as a more reliable and accurate pdf of tidal residuals than the normal function. This suggested pdf shows a good agreement with the empirical cumulative distribution function and histogram. It also gives the more accurate estimation result on the extreme values in comparison with the results based on the normal pdf assumption.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.245-255
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• 2011

There are two nonparametric methods that use empirical distribution functions and probability density estimators for the test of the distribution change of data. In this paper we investigate the two methods precisely and summarize the results of previous research. We assume several probability models to make a simulation study of the change point analysis and to examine the finite sample behavior of the two methods. Empirical powers are compared to verify which is better for each model.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.765-775
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• 2012

The cross-validation is a popular method to select bandwidth in all types of kernel estimation. The maximum likelihood cross-validation, the least squares cross-validation and biased cross-validation have been proposed for bandwidth selection in kernel density estimation. In the case that the probability density function has a discontinuity point, Huh (2012) proposed a method of bandwidth selection using the maximum likelihood cross-validation. In this paper, two forms of cross-validation with the one-sided kernel function are proposed for bandwidth selection to estimate the location and jump size of the discontinuity point of density. These methods are motivated by the least squares cross-validation and the biased cross-validation. By simulated examples, the finite sample performances of two proposed methods with the one of Huh (2012) are compared.

• Proceedings of the Korean Aquaculture Society Conference
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• 2006.05a
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• pp.194-195
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• 2006

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.895-906
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• 2015

Diffusion is a mathematical tool to explain the fluctuation of financial assets and the movement of particles in a micro time scale. There are ongoing statistical trials to develop an estimation method for diffusion models based on likelihood. When we estimate diffusion models by applying the maximum likelihood estimation method on data observed at discrete time points, we need to know the transition density of the diffusion. In order to approximate the transition densities of diffusion models, we suggests the method to approximate the path integral of the random process with normal random variables, and compare the numerical properties of the method with other approximation methods.

• 한국수산학회:학술대회논문집
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• 2005.05a
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• pp.112-114
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• 2005

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.55-63
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• 2019

In engineering problems, many random variables have correlation, and the correlation of input random variables has a great influence on reliability analysis results of the mechanical systems. However, correlated variables are often treated as independent variables or modeled by specific parametric joint distributions due to difficulty in modeling joint distributions. Especially, when there are insufficient correlated data, it becomes more difficult to correctly model the joint distribution. In this study, multivariate kernel density estimation with bounded data is proposed to estimate various types of joint distributions with highly nonlinearity. Since it combines given data with bounded data, which are generated from confidence intervals of uniform distribution parameters for given data, it is less sensitive to data quality and number of data. Thus, it yields conservative statistical modeling and reliability analysis results, and its performance is verified through statistical simulation and engineering examples.