• Communications for Statistical Applications and Methods
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• 제24권3호
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• pp.271-290
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• 2017

We study a bivariate response regression model with arbitrary marginal distributions and joint distributions using Frank and Clayton's families of copulas. The proposed model is used for fitting dependent bivariate data with explanatory variables using the log-odd log-logistic Weibull distribution. We consider likelihood inferential procedures based on constrained parameters. For different parameter settings and sample sizes, various simulation studies are performed and compared to the performance of the bivariate odd-log-logistic-Weibull regression model. Sensitivity analysis methods (such as local and total influence) are investigated under three perturbation schemes. The methodology is illustrated in a study to assess changes on schoolchildren's oral health-related quality of life (OHRQoL) in a follow-up exam after three years and to evaluate the impact of caries incidence on the OHRQoL of adolescents.

• Structural Engineering and Mechanics
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• 제70권1호
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• pp.1-11
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• 2019

This study deals with a method based on the modified bivariate gamma function for reconstructions of dynamic behavior of delaminated composite plates subjected to impact loads. The proposed bivariate gamma function is associated with micro-genetic algorithms, which is capable of solving inverse problems to determine the stiffness reduction associated with delamination. From computing the unknown parameters, it is possible for the entire dynamic response data to develop a prediction model of the dynamic response through a regression analysis based on the measurement data. The validity of the proposed method was verified by comparing with results employing a higher-order finite element model. Parametric results revealed that the proposed method can reconstruct dynamic responses and the stiffness reduction of delaminated composite plates can be investigated for different measurements and loading locations.

• 한국수자원학회논문집
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• 제52권11호
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• pp.905-916
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• 2019

본 연구에서는 이변량 웨이블릿 분석에 있어 모 웨이블릿이 어떤 영향을 미치는지를 파악하였다. 모 웨이블릿으로는 관련 연구에서 많이 사용되고 있는 총 네 가지(Bump, Mexican hat, Morlet, Paul)를 선정하였다. 이들 모 웨이블릿은 먼저 백색잡음과 다양한 주기의 사인곡선을 결합하여 만든 시계열의 이변량 분석에 적용하여 그 결과를 평가하였다. 또한 실제 시계열인 북극진동지수(AOI)와 남방진동지수(SOI)를 이변량 분석하여 모의된 시계열의 분석 결과가 실제 자료의 분석결과에도 일관되게 유지되는지를 판단하였다. 본 연구의 결과를 요약하면 다음과 같다. 먼저, Bump와 Morlet 모 웨이블릿의 경우가 이론적인 예측에 보다 잘 부합하는 것으로 나타났으며, 반대로 Mexican hat 모 웨이블릿은 상대적으로 단주기의 변동 특성을, Paul 모 웨이블릿의 경우에는 장주기의 변동 특성을 잘 보여주는 것으로 나타났다. 둘째, Mexican hat과 Paul 모 웨이블릿의 경우에는 스케일 간섭이 매우 크게 나타남을 확인할 수 있었다. Bump와 Morlet 모 웨이블릿에서는 이러한 문제점이 나타나지 않았다. 소위 동조화(co-movement)를 탐색하는 능력은 Morlet와 Paul 모 웨이블릿이 가지고 있는 것으로 파악되었다. 특히, Morlet의 경우 이 특성이 더욱 명확히 나타남을 확인하였다. 결과적으로 Morlet 모 웨이블릿이 이변량 웨이블릿 분석에 가장 무난한 것으로 확인되었다. 마지막으로, AOI와 SOI 자료의 이변량 웨이블릿 분석에서는 대략 2-4년 정도의 주기성분이 약 20년 빈도로 서로 동조하고 있음을 확인할 수 있었다.

• 한국유기농업학회지
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• 제17권4호
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• pp.413-439
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• 2009

This study conducts a survey and test to understand U.S. public's perception about multifunctionality. The questionnaire suggests seven alternative way of providing questions about intangible benefits provided by agriculture in the U.S. The final questionnaire was administered as an e-mail survey in June 2008 to a nationally representative household panel maintained in the U.S. by the Ipsos Observer. Data analysis shows that 64 percent of respondents considered the multifunctionality of agriculiture as an important issue and 45 percent of respondents were in favor of increasing government expenditure to support farmland preservation. Using Fishbein's multi-attribute model as a theoretical background, this paper develops an empirical model to assess and attributes of multifunctionality. For the analysis, bivariate orderd probit model was set up to reflect respondent's attitude. Regression analyses show that two questions (how much you agree with agriculture's intangible benefit and increasing government expenditure to support agriculture) are shaped by different sets of facts.

• International Journal of Reliability and Applications
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• 제13권1호
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• pp.37-47
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• 2012

In this article we attempted reliability analysis of a component under the stress-strength pattern with both classical as well as Bayesian techniques. The main focus is made to develop the theory for dealing the reliability problems in various circumstances for bivariate environmental set up in context of Bayesian paradigm. A stress-strength based model describes the life of a component which has strength (Y) and is subjected to stress(X). We develop the Bayes and moment estimators of reliability of a component for each of the three possible conditions, under the assumption that the two stresses (i.e. $X_1$ and $X_2$) on a component are dependent and follow a Bivariate exponential (BVE) of Marshall-Olkin distribution, the strength of a component (Y) following exponential distribution is independent of the stresses. The simulation study is performed with Markov Chain Monte Carlo technique via Gibbs sampler to obtain the estimates of Bayes estimators of reliability, are compared with moment estimators of reliabilities on the basis of absolute biases.

• International Journal of Reliability and Applications
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• 제1권1호
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• pp.65-79
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• 2000

This research considers a system which has an ultimate terminal event such as death, critical failure, bankruptcy together with a certain indicative events (temporary malfunction, special treatment, kind of defaults) that frequently occurs before the terminal event comes to the system. Some investigation of a model for the corresponding bivariate data of the system have been done with an explanation of the situation in terms of two continuous variables instead of continuous-discrete variables and some other properties. Also an analysis has been carried out to evaluate the effect of intermediate observation of occurrence of indicative event so that the result can be used for a possible suggestion of an intermediate observing schedule.

• Journal of the Korean Data and Information Science Society
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• 제23권4호
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• pp.851-858
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• 2012

We consider a bivariate Poisson regression model to analyze discrete count data when two dependent variables are present. We estimate the regression coefficients as sociated with several safety countermeasures. We use Markov chain and Monte Carlo techniques to execute some computations. A simulation and real data analysis are performed to demonstrate model fitting performances of the proposed model.

• 한국수자원학회논문집
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• 제42권9호
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• pp.725-736
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• 2009

홍수사상은 크게 첨두홍수량, 홍수용적, 지속기간 등과 같은 서로 상관된 세 가지의 요소로 정의될 수 있다. 그러나 그동안 수공학적 계획이나 설계, 운영 등을 위한 홍수빈도해석에서는 주로 첨두홍수량 한가지 요소에 초점을 맞추어 홍수빈도해석을 수행해 왔다. 이러한 단변량 홍수빈도해석은 서로 상관된 홍수사상 사이의 복잡한 확률적 거동을 분석하는 데 있어 한계를 가지고 있다. 따라서, 본 연구에서는 Gumbel 혼합모형을 이용한 이변량 홍수빈도해석을 수행하여 홍수심도를 평가하는 방안을 제시하였다. 소양강댐의 35개년 일유입량 자료와 대청댐의 28개년 일유입량자료에 대해 각각의 홍수사상을 분리하고, 분리한 홍수사상에 대해 첨두홍수량과 홍수용적 사이의 결합분포와 결합재현기간 등을 도출하였다. 또한 이러한 이변량 홍수빈도해석에 의해 도출된 홍수 특성을 단변량 홍수빈도해석의 결과와 비교함으로써, 홍수심도 평가에 있어 이변량 홍수빈도 해석기법의 적용성에 관하여 검토하였다.

• 한국방재학회 논문집
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• 제11권2호
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• pp.137-146
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• 2011

본 연구에서는 이변량 극치분포를 이용하여 연최대치 호우사상을 평가하였다. 이를 위해 특정 재현기간을 가지는 호우사상의 강우량을 비동시결합 재현기간, 동시결합 재현기간 그리고 구간조건부 결합재현기간의 세 가지를 이용하여 산정하였다. 이때, 결합재현기간별 호우사상의 값의 크기가 서로 다르게 산정되는 이유를 이변량 분포의 확률특성을 보여주는 사분면을 이용하여 설명하였다. 호우지속기간 24시간인 경우에 동시결합재현기간을 이용하여 산정한 확률강우량은 전통적인 방법으로 얻어진 강우지속기간 24시간의 확률강우량과 유사하게 나타났다. 이러한 결과는 전통적인 강우빈도해석의 제약사항을 극복하는데 도움이 될 것으로 보여진다. 이변량 빈도해석으로 얻어진 확률호우사상은 저류시설물의 계획시 통계적으로 보다 유용하면서도 간단한 설계 호우사상을 제공할 수 있을 것으로 보여진다.

• Smart Structures and Systems
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• 제21권5호
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.