• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.191-204
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• 2006

This paper develops a Bayesian method to derive the optimal sequential preventive maintenance(PM) policy by determining the PM schedules which minimize the mean cost rate. Such PM schedules are derived based on a general sequential imperfect PM model proposed by Lin, Zuo and Yam(2000) and may have unequal length of PM intervals. To apply the Bayesian approach in this problem, we assume that the failure times follow a Weibull distribution and consider some appropriate prior distributions for the scale and shape parameters of the Weibull model. The solution is proved to be finite and unique under some mild conditions. Numerical examples for the proposed optimal sequential PM policy are presented for illustrative purposes.

• Journal of the Korean Statistical Society
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• 제35권2호
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• pp.143-156
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• 2006

In the present paper, various solutions for generalized canonical correlation analysis (GCCA) are considered depending on the criteria and constraints. For the comparisons of some characteristics of the solutions, we provide with certain unifying stationary equations which might to also useful to obtain various generalized canonical correlation analysis solutions. In addition, we suggest an approach for the generalized canonical correlation analysis by exploiting the concept of maximum eccentricity originally de-signed to test the internal independence structure. The solutions, including new one, are compared through unifying stationary equations and by using some numerical illustrations. A type of iterative procedure for the GCCA solutions is suggested and some numerical examples are provided to illustrate several GCCA methods.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.43-60
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• 2018

This paper considers the use of classical and Bayesian inference methods to analyze data generated by variables whose natural behavior can be modeled using asymmetric distributions in the presence of left censoring. Our approach used a $L{\grave{e}}vy$ distribution in the presence of left censored data and covariates. This distribution could be a good alternative to model data with asymmetric behavior in many applications as lifetime data for instance, especially in engineering applications and health research, when some observations are large in comparison to other ones and standard distributions commonly used to model asymmetry data like the exponential, Weibull or log-logistic are not appropriate to be fitted by the data. Inferences for the parameters of the proposed model under a classical inference approach are obtained using a maximum likelihood estimators (MLEs) approach and usual asymptotical normality for MLEs based on the Fisher information measure. Under a Bayesian approach, the posterior summaries of interest are obtained using standard Markov chain Monte Carlo simulation methods and available software like SAS. A numerical illustration is presented considering data of thyroglobulin levels present in a group of individuals with differentiated cancer of thyroid.

• Communications for Statistical Applications and Methods
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• 제28권2호
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• pp.205-215
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• 2021

Sufficient dimension reduction is useful dimension reduction tool in regression, and sliced inverse regression (Li, 1991) is one of the most popular sufficient dimension reduction methodologies. In spite of its popularity, it is known to be sensitive to the number of slices. To overcome this shortcoming, the so-called fused sliced inverse regression is proposed by Cook and Zhang (2014). Unfortunately, the two existing methods do not have the direction application to large p-small n regression, in which the dimension reduction is desperately needed. In this paper, we newly propose seeded sliced inverse regression and seeded fused sliced inverse regression to overcome this deficit by adopting iterative projection approach (Cook et al., 2007). Numerical studies are presented to study their asymptotic estimation behaviors, and real data analysis confirms their practical usefulness in high-dimensional data analysis.

• Smart Structures and Systems
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• 제20권2호
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• pp.207-217
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• 2017

Because of the inevitable uncertainties such as structural parameters, external excitations and measurement noises, the effects of uncertainties should be taken into consideration in structural damage detection. In this paper, two probabilistic structural damage detection approaches are proposed to account for the underlying uncertainties in structural parameters and external excitation. The first approach adopts the statistical moment-based structural damage detection (SMBDD) algorithm together with the sensitivity analysis of the damage vector to the uncertain parameters. The approach takes the advantage of the strength SMBDD, so it is robust to measurement noise. However, it requests the number of measured responses is not less than that of unknown structural parameters. To reduce the number of measurements requested by the SMBDD algorithm, another probabilistic structural damage detection approach is proposed. It is based on the integration of structural damage detection using temporal moments in each time segment of measured response time history with the sensitivity analysis of the damage vector to the uncertain parameters. In both approaches, probability distribution of damage vector is estimated from those of uncertain parameters based on stochastic finite element model updating and probabilistic propagation. By comparing the two probability distribution characteristics for the undamaged and damaged models, probability of damage existence and damage extent at structural element level can be detected. Some numerical examples are used to demonstrate the performances of the two proposed approaches, respectively.

• 한국터널지하공간학회 논문집
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• 제8권3호
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• pp.249-257
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• 2006

지반 설계 인자는 공간적으로 분포하는 특성이 있으며, 이는 터널의 설계와 시공과정 뿐만 아니라 장기 거동에 큰 영향을 미친다. 그러나 일반적으로 터널의 수치해석을 위한 설계 지반 인자는 대상 영역을 대표하는 값 또는 대상 영역의 광역적 평균값 등이 적용되고 있다. 특히, 지하공간의 크기가 증가할수록 설계 지반 인자의 불확실성 및 공간적 분포 또한 증가한다. 결국 이러한 불확실성과 공간적 분포 특성의 확대는 해석의 정확성 및 신뢰성 저하를 초래하게 된다. 따라서, 대형 터널의 구조적 안정성을 확보하기위해 지반 물성치들의 공간적인 분포에 대한 정량적인 조사가 설계시 포함되어야 한다. 본 연구에서는 지반 물성치 및 구조적 설계 인자의 공간적 분포가 터널의 변위에 미칠 수 있는 영향을 분석하였다. 여러 COV(Coefficient of Variation)에 따라 정규분포하는 지반 물성치의 공간적 특성이 이상화된 원형 터널의 변위에 미치는 영향에 대한 분석과, NATM(New Austrian Tunneling Method) 터널에서 숏크리트의 강도의 공간적 분포가 터널 변위에 미치는 영향을 분석하였다. 공간적 분포의 COV가 증가할수록 터널 주변 발생하는 변위량도 증가하는 것으로 나타났으며, 분석 결과 이들은 지반 물성치에 따라 고유한 계수를 갖는 삼차방정식으로 표현된다.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.651-662
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• 2017

We propose an alternative procedure to select penalty parameter in $L_1$ penalized robust regression. This procedure is based on marginalization of prior distribution over the penalty parameter. Thus, resulting objective function does not include the penalty parameter due to marginalizing it out. In addition, its estimating algorithm automatically chooses a penalty parameter using the previous estimate of regression coefficients. The proposed approach bypasses cross validation as well as saves computing time. Variable-wise penalization also performs best in prediction and variable selection perspectives. Numerical studies using simulation data demonstrate the performance of our proposals. The proposed methods are applied to Boston housing data. Through simulation study and real data application we demonstrate that our proposals are competitive to or much better than cross-validation in prediction, variable selection, and computing time perspectives.

• 수산해양기술연구
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• 제37권1호
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• pp.35-44
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• 2001

본 논문에서 취급한 계산모델 및 계산조건하에서 얻어진 주요한 결론은 다음과 같다. (1) 다방향 불규칙파중에서 TLP에 작용하는 파강제력 및 정상표류력의 유의치를 구할 수 있는 프로그램을 개발하였다. (2) 한방향파중에서 파강제력 및 정상표류력이 큰 모-드에 대해서는 다방향파의 영향으로 감소하는 경향을 보이고, 한방향파중에서 작은 모-드에 대해서는 다방향파의 영향이 무시할 수 없을 정도로 나타났다. (3) 다방향파의 상호작용에 의해 실해역을 재현할 수 있으며, 다방향파의 영향으로 최대 파강제력 및 정상표류력의 크기가 감소한다는 결과에 따라 다방향파의 영향을 고려하면 보다 현실적인 결과를 얻을 수 있을 것으로 사료된다.

• Communications for Statistical Applications and Methods
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• 제20권2호
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• pp.147-155
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• 2013

To determine whether each coefficient is equal to zero or not, usual $t$-tests are a popular choice (among others) in linear regression to practitioners because all statistical packages provide the statistics and their corresponding $p$-values. Under smaller samples (especially with non-normal errors) the tests often fail to correctly detect statistical significance. We propose a permutation approach by adopting a sufficient dimension reduction methodology to overcome this deficit. Numerical studies confirm that the proposed method has potential advantages over the t-tests. In addition, data analysis is also presented.

• Computers and Concrete
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• 제19권5호
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• pp.527-535
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• 2017

In this study, an aging deformation statistical model for a unique high and steep rock slope was proposed, and the aging characteristic of the slope deformation was better reflected. The slope displacement was affected by multiple-environmental factors in multiple scales and displayed the same tendency with a rising water level. The statistical model of the high and steep rock including non-aging factors was set up based on previous analyses and the study of the deformation and residual tendency. The rule and importance of the water level factor as a non-aging unit was analyzed. A partitioned statistical model and mutation model were established for the comprehensive cumulative displacement velocity with the monitoring study under multiple factors and multiple parameters. A spatial model was also developed to reflect and predict the whole and sectional deformation character by combining aging, deformation and space coordinates. A neural network model was built to fit and predict the deformation with a high degree of precision by mastering its feature of complexity and randomness. A three-dimensional finite element model of the slope was applied to approach the structure character using numerical simulations. Further, a three-dimensional finite element model of the slope and dam was developed, and the whole deformation state was analyzed. This study is expected to provide a powerful and systematic method to analyze very high, important and dangerous slopes.