• Earthquakes and Structures
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• 제14권3호
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• pp.203-214
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• 2018

Predictive demand and collapse fragility functions are two essential components of the probabilistic seismic demand analysis that are commonly developed based on statistics with enormous, costly and time consuming data gathering. Although this approach might be justified for research purposes, it is not appealing for practical applications because of its computational cost. Thus, in this paper, Bayesian regression-based demand and collapse models are proposed to eliminate the need of time-consuming analyses. The demand model developed in the form of linear equation predicts overall maximum inter-story drift of the lowto mid-rise regular steel moment resisting frames (SMRFs), while the collapse model mathematically expressed by lognormal cumulative distribution function provides collapse occurrence probability for a given spectral acceleration at the fundamental period of the structure. Next, as an application, the proposed demand and collapse functions are implemented in a seismic fragility analysis to develop fragility and consequently seismic demand curves of three example buildings. The accuracy provided by utilization of the proposed models, with considering computation reduction, are compared with those directly obtained from Incremental Dynamic analysis, which is a computer-intensive procedure.

• 한국수자원학회논문집
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• 제48권10호
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• pp.793-806
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• 2015

미계측유역의 유출량 모의는 수문학 분야에서 필수적인 사항이다. 강우-유출 모형을 이용하여 신뢰성 있는 유출량을 모의하기 위한 핵심사항은 강우-유출 모형의 매개변수를 추정하는 것이다. 하지만 현재 우리나라는 불충분한 수문자료로 인해 매개변수 추정에 어려움이 존재한다. 본 연구의 목표는 불확실성 반영을 위한 Bayesian 통계기법 기반의 강우-유출 모형의 매개변수를 지역화 하는 것이다. 그 방법은 다음과 같다. 첫째, 본 연구는 세계적으로 널리 사용되고 있는 Sacramento 강우-유출 모형에 Bayesian Markov Chain Monte Carlo 기법을 연계한 Bayesian Sacramento 강우-유출 모형을 사용하여 계측유역을 대상으로 13개 매개변수를 최적화하고 각 매개변수의 사후분포를 도출하였다. 둘째, 매개변수와 유역특성인자 사이에 회귀특성을 얻기 위해 다중선형회귀분석을 적용하여 유역특성을 고려한 지역화 매개변수를 결정하였다. 다중회귀분석을 통하여 산정된 지역화 매개변수를 계측유역에 전이하여 유출량을 모의 후 통계적 효율기준인 N-S계수, 일치계수 및 상관계수를 사용하여 지역화 매개변수 검증을 수행하였다.

• Journal of the Korean Statistical Society
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• 제22권2호
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• pp.209-217
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• 1993

The multicollinearity problem in a multiple linear regression model may present deleterious effects on predictions. Thus, its is desirable to consider the optimal fractions with respect to the unbiased estimate of the mean squares errors of the predicted values. Interstingly, the optimal fractions can be also illuminated by the Bayesian inerpretation of the general James-Stein estimators.

• 한국수자원학회논문집
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• 제52권9호
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• pp.589-601
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• 2019

최근 수문기상학 분야에서 레이더 강수량을 활용한 응용연구가 활발하게 진행되고 있다. 하지만 레이더 강수량은 경험적인 레이더 반사도-강수강도 관계식을 활용하여 레이더 강수량을 추정하기 때문에 실제 지상에 도달하는 강수량과 정량적인 오차가 필연적으로 발생한다. 따라서 본 연구에서는 레이더 강수량 편의보정을 위하여 Bayesian 추론기법과 일반화 선형모형을 연계하여 불확실성을 고려한 편의보정 매개변수를 산정하였다. 일반화 선형모형을 적용한 레이더 강수량 편의보정 결과는 현재 널리 사용되고 있는 평균보정 기법보다 우수한 통계적 효율기준을 제시하였다. 추가로 지형학적 특성에 따른 편의보정 매개변수의 변동성을 분석하여 고도 및 이격거리에 따른 편의보정 매개변수의 지역화 공식을 제시하였다. 본 연구를 통하여 개발된 레이더 강수량 편의보정 매개변수 산정 및 지역화 결과는 레이더와 관련된 다양한 연구에 활용성이 클 것으로 판단된다.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.625-637
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• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• Communications for Statistical Applications and Methods
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• 제24권4호
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• 대기
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• 제18권2호
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• pp.111-120
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• 2008

This study presents a change-point in the 30 years (1976-2005) time series of the annual and the heavy precipitation characteristics (amount, days and intensity) averaged over South Korea using Bayesian approach. The criterion for the heavy precipitation used in this study is 80 mm/day. Using non-informative priors, the exact Bayes estimators of parameters and unknown change-point are obtained. Also, the posterior probability and 90% highest posterior density credible intervals for the mean differences between before and after the change-point are examined. The results show that a single change-point in the precipitation intensity and the heavy precipitation characteristics has occurred around 1996. As the results, the precipitation intensity and heavy precipitation characteristics have clearly increased after the change-point. However, the annual precipitation amount and days show a statistically insignificant single change-point model. These results are consistent with earlier works based on a simple linear regression model.

• Communications for Statistical Applications and Methods
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• 제28권4호
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• pp.315-327
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• 2021

The Spatial Autoregressive (SAR) models have drawn considerable attention in recent econometrics literature because of their capability to model the spatial spill overs in a feasible way. While considering the Bayesian analysis of these models, one may face the problem of lack of robustness with respect to underlying prior assumptions. The generalized Bayes estimators provide a viable alternative to incorporate prior belief and are more robust with respect to underlying prior assumptions. The present paper considers the SAR model with a set of linear restrictions binding the regression coefficients and derives restricted generalized Bayes estimator for the coefficients vector. The minimaxity of the restricted generalized Bayes estimator has been established. Using a simulation study, it has been demonstrated that the estimator dominates the restricted least squares as well as restricted Stein rule estimators.

• 수산해양기술연구
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• 제60권1호
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• pp.87-99
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• 2024

This study deals with the application of an artificial neural network (ANN) model to predict power consumption for utilizing seawater source heat pumps of recirculating aquaculture system. An integrated dynamic simulation model was constructed using the TRNSYS program to obtain input and output data for the ANN model to predict the power consumption of the recirculating aquaculture system with a heat pump system. Data obtained from the TRNSYS program were analyzed using linear regression, and converted into optimal data necessary for the ANN model through normalization. To optimize the ANN-based power consumption prediction model, the hyper parameters of ANN were determined using the Bayesian optimization. ANN simulation results showed that ANN models with optimized hyper parameters exhibited acceptably high predictive accuracy conforming to ASHRAE standards.

• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.713-722
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• 2009

The estimation of odds ratio and corresponding confidence intervals for case-control data have been done by traditional generalized linear models which assumed that the logarithm of odds ratio is linearly related to risk factors. We adapt a lower-dimensional approximation of Gu and Kim (2002) to provide a faster computation in nonparametric method for the estimation of odds ratio by allowing flexibility of the estimating function and its Bayesian confidence interval under the Bayes model for the lower-dimensional approximations. Simulation studies showed that taking larger samples with the lower-dimensional approximations help to improve the smoothing spline estimates of odds ratio in this settings. The proposed method can be used to analyze case-control data in medical studies.