• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.219-226
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• 2004

In this paper, we consider two-components system which the lifetimes have a bivariate Weibull distribution with bivariate random censored data. Here the bivariate censoring times are independent of the lifetimes of the components. We obtain estimators and approximated confidence intervals for the reliability of series system based on likelihood function and relative frequency, respectively. Also we present a numerical study.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.405-412
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• 2003

In this paper, we consider two components system which the lifetimes have a bivariate weibull distribution with random censored data. Here the censoring time is independent of the lifetimes of the components. We construct large sample tests for independence and symmetry between two-components based on maximum likelihood estimators and the natural estimators. Also we present a numerical study.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.523-544
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• 2018

Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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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.

• Smart Structures and Systems
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• v.21 no.5
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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.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.51-56
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• 1999

In this paper, we study the estimation of system reliability for the parallel system based on Spurrier and Weier 〔9〕 bivariate Weibull distribution. We assume that when one component fails, the workload of the remaining component becomes proportional to ψλ, where ψ〉0. We obtain the maximum likelihood estimators for the parameters of system reliability, and by using the numerical method, study the effects of reliability for the parallel system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.762-766
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• 2002

This paper proposes a method or estimating lifetime distribution for products under two-dimensional warranty in which age and usage are used simultaneously to determine the eligibility of a warranty claim. For such a case, existing methods reduce the two-dimensional time stale to a single stale assuming that the two variables have a functional relationship. This assumption is, however, not appropriate since the functional relationship is unknown in practice. In this paper, the field age and usage data are modeled with a bivariate lifetime distribution. Method of obtaining maximum likelihood estimators is outlined, their asymptotic properties are studied and specific formulas for a bivariate Weibull distribution are obtained. The proposed model is compared with the existing one which assumes a lineal relationship between the two variables Simulation studios are performed to investigate the effect of the degree of dependency between the two variables.

• Journal of Applied Reliability
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• v.11 no.4
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• pp.319-330
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• 2011

We consider the stress-strength model in which a unit of strength $T_2$ is subjected to environmental stress $T_1$. An important measure considered in stress-strength model is the reliability parameter R=P($T_2$ > $T_1$). The greater the value of R is, the more reliable is the unit to perform its specified task. In this article, we consider the situations in which $T_1$ and $T_2$ are both independent and dependent, and have certain bivariate distributions as their joint distributions. To study the effect of dependency on R, we investigate several bivariate distributions of $T_1$ and $T_2$ and compare the values of R for these distributions. Numerical comparisons are presented depending on the parameter values as well.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.335-335
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• 2019

최근 수문자료에 대한 다변량 빈도해석 연구가 활발히 이루어지고 있다. 하나의 자료를 확률변수로 사용하는 단변량 빈도해석에 비해 여러 수문자료를 조합하여 동시에 추정할 수 있는 다변량 빈도해석은 수문자료의 상관성을 고려하면서 확률분포형을 추정할 수 있다는 장점이 있다. 이에 다변량 확률분포형을 이용한 빈도해석 과정 중 정확한 매개변수 추정을 위한 연구도 최근 여러방면으로 이루어지고 있다. 본 연구에서는 다변량 확률분포형의 매개변수 추정방법 중 기존에 주로 사용되고 있는 의사최우도법(MPL, Maximum Pseudo-Likelihood method)의 성능을 개선하기 위해 기존의 방법과 본 연구에서 제안하는 매개변수 추정방법의 Monte-Carlo 모의실험을 수행하였다. 일반적으로 수문자료는 양(+)의 왜곡도계수를 갖기 때문에 GEV(Geveralized Extreme Value) 분포형을 모분포로 하여 각 방법의 정확성을 검토하였다. 모의실험을 수행한 결과, 기존의사최우도법에서 Weibull 식을 이용하여 순위통계량을 계산하는 방법보다 본 연구에서 제안한 왜곡도를 고려하는 순위통계량을 사용하는 것이 더 정확한 매개변수 추정결과를 보여주는 것으로 나타났다.