• 대한수학교육학회지:학교수학
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• 제19권3호
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• pp.533-551
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• 2017

통계교육 연구자들은 형식적 추리 방법을 지도하기에 앞서 비형식적 추리를 지도할 것을 강조하며 통계적 추리의 발달 과정에 주목하고 있다. 본 연구는 표본 비교하기 과제와 모집단의 그래프 추측하기 과제를 해결하는 과정에서 나타나는 중 고등학생들의 비형식적 통계적 추리의 수준과 각 수준별 특징을 분석하였다. 연구 결과, 표본 비교하기 과제에서는 개인적인 의견에 기초하여 타당하지 않은 추리를 하는 수준, 자료에 대한 국소적 관점을 가진 수준, 자료에 대한 전체적 관점으로 전환되는 수준, 분포의 다각적인 측면에 주목하는 수준, 통계적 개념들을 통합하여 추리하는 수준이 확인되었다. 모집단의 그래프 추측하기 과제에서는 개인적인 의견에 기초하여 타당하지 않은 추리를 하는 수준, 표본대표성에만 주목하고 표집변이성을 고려하지 않는 수준, 표본대표성과 표집변이성을 모두 고려하며 분포의 한 측면에 주목하여 부분적으로 타당한 추리를 하는 수준, 분포의 다각적 측면에 주목하는 수준, 통계적 개념들을 통합하여 추리하는 수준이 확인되었다.

• Journal of the Korean Statistical Society
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• 제17권2호
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• pp.81-92
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• 1988

We observe independent random variable $Y_i \sim N(\theta_i,1), i=1,2,\cdots,k$, and we are interested in detecting the treatment with the largest $\theta_i$. We consider a procedure which infers $\theta_{(k)} \geq max\theta_i (i\neq(k))$ whenever $Y_{(k)} \geq maxY_i+C (i\neq(k))$. The maximum probability of a false inference is found, and it is shown that the inference can be made with the two-sample one-sided critical value for the usual error levels. The result also holds in the case of common unknown variance.

• Communications for Statistical Applications and Methods
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• 제25권5호
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• pp.559-568
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• 2018

Gene classification can involve complex order-restricted inference. Examining gene expression pattern across groups with order-restriction makes standard statistical inference ineffective and thus, requires different methods. For this problem, Roy's union-intersection principle has some merit. The M-estimator adjusting for outlier arrays in a microarray study produces a robust test statistic with distribution-insensitive clustering of genes. The M-estimator in conjunction with a union-intersection principle provides a nonstandard robust procedure. By exact permutation distribution theory, a conditionally distribution-free test based on the proposed test statistic generates corresponding p-values in a small sample size setup. We apply a false discovery rate (FDR) as a multiple testing procedure to p-values in simulated data and real microarray data. FDR procedure for proposed test statistics controls the FDR at all levels of ${\alpha}$ and ${\pi}_0$ (the proportion of true null); however, the FDR procedure for test statistics based upon normal theory (ANOVA) fails to control FDR.

• Journal of the Korean Statistical Society
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• 제30권1호
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• pp.1-20
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• 2001

We present how bootstrap methods can be used to conduct inference on the rates of Poisson distributions when only the grouped data are available. A theoretical justification for the validity of bootstrap is given with an illustration of proposed method using a data set obtained fro ma pathology laboratory test. Traditional asymptotic methods are compared with bootstrap methods in computing the estimated standard errors and achieved significance levels for one sample and two sample tests. Bootstrap methods are shown to possess a nice property that he small sample distribution of the relevant statistics can be readily obtained from the bootstrap copies.

• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.239-250
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• 2022

In many applications, we frequently encounter correlated multiple outcomes measured on the same subject. Joint modeling of such multiple outcomes can improve efficiency of inference compared to independent modeling. For instance, in developmental toxicity studies, fetal weight and number of malformed pups are measured on the pregnant dams exposed to different levels of a toxic substance, in which the association between such outcomes should be taken into account in the model. The number of malformations may possibly have many zeros, which should be analyzed via zero-inflated count models. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint modeling framework for continuous and count outcomes with excess zeros. In our model, zero-inflated Poisson (ZIP) regression model would be used to describe count data, and a subject-specific random effects would account for the correlation across the two outcomes. We implement a Bayesian approach using MCMC procedure with data augmentation method and adaptive rejection sampling. We apply our proposed model to dose-response analysis in a developmental toxicity study to estimate the benchmark dose in a risk assessment.

• Structural Engineering and Mechanics
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• 제69권3호
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• pp.317-326
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• 2019

This research aims to assess the tight seismic risk curve of the intake tower at Geumgwang reservoir by considering the recorded historical earthquake data in the Korean Peninsula. The seismic fragility, a significant part of risk assessment, is updated by using Bayesian inference to consider the uncertainties and computational efficiency. The reservoir is one of the largest reservoirs in Korea for the supply of agricultural water. The intake tower controls the release of water from the reservoir. The seismic risk assessment of the intake tower plays an important role in the risk management of the reservoir. Site-specific seismic hazard is computed based on the four different seismic source maps of Korea. Probabilistic Seismic Hazard Analysis (PSHA) method is used to estimate the annual exceedance rate of hazard for corresponding Peak Ground Acceleration (PGA). Hazard deaggregation is shown at two customary hazard levels. Multiple dynamic analyses and a nonlinear static pushover analysis are performed for deriving fragility parameters. Thereafter, Bayesian inference with Markov Chain Monte Carlo (MCMC) is used to update the fragility parameters by integrating the results of the analyses. This study proves to reduce the uncertainties associated with fragility and risk curve, and to increase significant statistical and computational efficiency. The range of seismic risk curve of the intake tower is extracted for the reservoir site by considering four different source models and updated fragility function, which can be effectively used for the risk management and mitigation of reservoir.

• 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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• 제27권4호
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• pp.459-468
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• 2020

We examine the rate of substitution of jobs by artificial intelligence using a score called the "weighted ability rate of substitution (WARS)." WARS is a indicator that represents each job's potential for substitution by automation and digitalization. Since the conventional WARS is sensitive to the particular responses from the employees, we consider a robust version of the indicator. In this paper, we propose the individualized WARS, which is a modification of the conventional WARS, and compute robust averages and confidence intervals for inference. In addition, we use the clustering method to statistically classify jobs according to the proposed individualized WARS. The proposed method is applied to Korean job data, and proposed WARS are computed for five future years. Also, we observe that 747 jobs are well-clustered according to the substitution levels.

• 한국수자원학회논문집
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• 제44권7호
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• pp.537-551
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• 2011

본 연구에서는 앞선 연구를 통해 선정된 최적 입력 자료 조합을 이용하여 한강수계의 왕숙천과 금강유역의 갑천에 대한 Takagi-Sugeno 퍼지기법과 신경망을 연계한 뉴로-퍼지 홍수예측 모형을 구축하였다. 구축된 뉴로-퍼지 홍수예측 모형을 한강수계의 왕숙천과 금강유역의 갑천에 적용하여 30분, 60분, 90분, 120분, 150분, 180분의 선행시간에 대해 각각 홍수예측을 수행하였다. 선행시간별 예측수위를 관측수위와 비교한 결과 안정되고 정확도 높은 홍수예측을 하는 것을 확인할 수 있었다. 추가적으로 정량적 평가를 위해 평균제곱근 오차(Root Mean Square Error)와 같은 통계지표를 산정하여 모형의 적용성을 검증하였다. 검증 결과 모든 통계지표에서 큰 오차 없이 성공적으로 홍수예측이 모의됨을 확인할 수 있었다. 본 연구결과는 향후 중소하천에서 충분한 선행시간을 확보한 정확도 높은 홍수정보시스템의 구축에 활용할 수 있을 것으로 판단된다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제4권2호
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• pp.112-122
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• 2012

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.