• Communications for Statistical Applications and Methods
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• 제15권3호
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• pp.429-440
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• 2008

In this paper, we develop the noninformative priors when the parameter of interest is the common coefficient of variation in two inverse Gaussian distributions. We want to develop the first and second order probability matching priors. But we prove that the second order probability matching prior does not exist. It turns out that the one-at-a-time and two group reference priors satisfy the first order matching criterion but Jeffreys' prior does not. The Bayesian credible intervals based on the one-at-a-time reference prior meet the frequentist target coverage probabilities much better than that of Jeffreys' prior. Some simulations are given.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.531-542
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• 2015

In this survey, we use the normal linear model to demonstrate the use of the linear Bayes method. The superiorities of linear Bayes estimator (LBE) over the classical UMVUE and MLE are established in terms of the mean squared error matrix (MSEM) criterion. Compared with the usual Bayes estimator (obtained by the MCMC method) the proposed LBE is simple and easy to use with numerical results presented to illustrate its performance. We also examine the applications of linear Bayes method to some other distributions including two-parameter exponential family, uniform distribution and inverse Gaussian distribution, and finally make some remarks.

• Wind and Structures
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• 제31권6호
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• pp.549-560
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• 2020

Methods for stochastic simulation of non-Gaussian wind pressure have increasingly addressed the efficiency and accuracy contents to offer an accurate description of the extreme value estimation of the long-span and high-rise structures. This paper presents a linear prediction and z-transform (LPZ) based Cumulative distribution function (CDF) mapping algorithm for the simulation of multivariate non-Gaussian fluctuating wind pressure. The new algorithm generates realizations of non-Gaussian with prescribed marginal probability distribution function (PDF) and prescribed spectral density function (PSD). The inverse linear prediction and z-transform function (ILPZ) is deduced. LPZ is improved and applied to non-Gaussian wind pressure simulation for the first time. The new algorithm is demonstrated to be efficient, flexible, and more accurate in comparison with the FFT-based method and Hermite polynomial model method in two examples for transverse softening and longitudinal hardening non-Gaussian wind pressures.

• Journal of the Korean Statistical Society
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• 제13권2호
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• pp.73-80
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• 1984

In this paper, likelihood-ratio test has been derived for testing multisample sphericity in complex multivariate Gaussian populations. The $h^{th}$ moment of the test statistic is given and its exact distribution has been derived using inverse Mellin transform. Asymptotic distribution of the statistic is also given.

• 복합신소재구조학회 논문집
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• 제1권2호
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• pp.51-58
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• 2010

본 연구는 차량 이동하중을 받는 손상된 콘크리트 슬래브교량의 강성저하를 규명하고, 복합신소재를 사용하여 보강 후 성능평가를 수행한다. 특히 마이크로 유전알고리즘에 의한 역해석에 기반하여 보강 전 후 각 요소에서의 강성변화를 수정된 2차변수 Gaussian 분포함수를 사용하여 정식화하였다. 본 연구에서 제안한 방법은 기존의 요소기반 접근 방식에 비하여 수치해석적인 관점에서 효율성을 갖는다. 개발한 알고리즘은 3차원 솔리드 요소를 사용하여 모델링한 교량의 동적 거동 시뮬레이션으로부터 계측한 데이터를 사전정보로 사용하여 검증하였다. 몇 가지 수치예제는 본 연구에서 개발한 방법이 실제교량과 수치모델간의 차이로 인한 오차 및 노이즈 등으로 인한 동적 계측치 오류 등이 고려되었음에도 강성분포 추정 및 성능 평가를 효율적으로 수행함을 보여준다.

• 한국광학회지
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• 제8권2호
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• pp.95-98
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• 1997

Gauss 초기함수 $e^{-\sigma}$$^{2}$$x^{2}$/에 $e^{-q{\omega}}$$_{0}$ vertical bar x vertical bar를 곱하여 선폭을 더욱 예리하게 하여 역문제(Inverse Problem)로 동함수를 구하고 다시 유한구경에서 결상하는 광파의 회절강도 분포를 구하였다. 이 광학계의 OTF를 구하고 이 값을 변조하지 않은 Gauss 동함수의 (OTF)$_{1.0}$ 의 값과 비교한 결과 변조된 Gauss 동함수의 OTF가 (OTF)$_{1.0}$ 보다 같거나 작음을 보였다. 회절 역문제취급에서 Gauss함수의 선폭을 무조건 예리하게 한다하여 OTF가 향상되지 않는다는 사실을 증명하였다.

• Communications for Statistical Applications and Methods
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• 제28권1호
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• pp.59-79
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• 2021

Value at Risk (VaR) is one of the most common risk management tools in finance. Since a portfolio of several assets, rather than one asset portfolio, is advantageous in the risk diversification for investment, VaR for a portfolio of two or more assets is often used. In such cases, multivariate distributions of asset returns are considered to calculate VaR of the corresponding portfolio. Copulas are one way of generating a multivariate distribution by identifying the dependence structure of asset returns while allowing many different marginal distributions. However, they are used mainly for bivariate distributions and are not widely used in modeling joint distributions for many variables in finance. In this study, we would like to examine the performance of various copulas for high dimensional data and several different dependence structures. This paper compares copulas such as elliptical, vine, and hierarchical copulas in computing the VaR of portfolios to find appropriate copula functions in various dependence structures among asset return distributions. In the simulation studies under various dependence structures and real data analysis, the hierarchical Clayton copula shows the best performance in the VaR calculation using four assets. For marginal distributions of single asset returns, normal inverse Gaussian distribution was used to model asset return distributions, which are generally high-peaked and heavy-tailed.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제2B권4호
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• pp.168-173
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• 2002

A new identification algorithm for the Preisach model is presented. The algorithm treats the identification procedure of the Preisach model as an inverse problem where the independent variables are parameters of the distribution function and the objective function is constructed using only the initial magnetization curve or only tile major loop of the hysteresis curve as well as the whole reversal curves. To parameterize the distribution function, the Bezier spline and Gaussian function are used for the coercive and interaction fields axes, respectively. The presented algorithm is applied to the ferrite permanent magnets, and the distribution functions are correctly found from the major loop of the hysteresis curve or the initial magnetization curve.

• 한국음향학회지
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• 제13권6호
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• pp.66-74
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• 1994

잔향음이 우세한 환경하에서 소나 시스템의 성능을 평가할 때 대부분의 음향 모델인 음압 준위(power level) 모델에 비해 수중 잔향음 신호 모의 모델은 매우 유용하게 이용된다. 본 논문에서는 소나 시험에 필요한 잔향음 신호(reverberation time series)를 모의하였다. 우선 주파수 영역에서 밴드의 폭이 변하고 가우시안 분포(Gaussian distribution)를 갖는 표준화된 스펙트럼(normalized spectrum)을 수신 시간 간격에 따라 구하였다. 두번째로, 기존의 음향 모델의 결과인 시간에 따른 잔향음 준위를 각각의 표준화된 스펙트럼의 준위와 합성하였다. 마지막으로 음원 신호의 스펙트럼과 잔향음 스펙트럼을 곱(product)하고 이 결과를 역푸리에 변환(inverse Fourier transform)을 이용하여 신호를 모의하였다.

• The Journal of the Acoustical Society of Korea
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• 제15권4E호
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• pp.3-8
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• 1996

The bubble size distribution is critical information to understand sound propagation and ambient noise in the ocean. To estimate the bubble size distribution in a bubbly water, the sound attenuation has been only in the conventional acoustic bubble sizing method without considering the sound speed variation. However, the effect of the sound speed variation in bubbly water cannot be neglected because of its compressibility variation. The sound attenuation is also affected by the sound speed variation. In this paper, a coherent acoustic bubble sizing inverse technique is introduced as a new bubble sizing technique with considering sound speed variation as well as the sound attenuation. This coherent sizing method is theoretically verified with the bubble distribution functions of single-size, Gaussian, and power-law functions. Its numerical test results with the coherent acoustic bubble sizing method show good agreement with the given bubble distributions.