• Nuclear Engineering and Technology
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• 제53권12호
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• pp.4042-4051
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• 2021

In this work, we applied a multilevel modeling technique to estimate the diametral creep in the pressure tubes of Korean Canada Deuterium Uranium (CANDU) units. Data accumulated from in-service inspections were used to develop the model. To confirm the strength of the multilevel models, a 2-level multilevel model considering the relationship between channels for a CANDU unit was compared with existing linear models. The multilevel model exhibited a very robust prediction accuracy compared to the linear models with different data pooling methods. A 3-level multilevel model, which considered individual bundles, channels, and units, was also implemented. The influence of the channel installation direction was incorporated into the three-stage multilevel model. For channels that were previously measured, the developed 3-level multilevel model exhibited a very good predictive power, and the prediction interval was very narrow. However, for channels that had never been measured before, the prediction interval widened considerably. This model can be sufficiently improved by the accumulation of more data and can be applied to other CANDU units.

• 분석과 대안
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• 제5권1호
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• pp.3-24
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• 2021

This paper introduces a multilevel item-response-theory (IRT) model as a unifying model for hypothesis testing using legislative voting data. This paper shows that a probit or logit model is a special type of multilevel IRT model. In particular, it is demonstrated that, when a probit or logit model is applied to multiple votes, it makes unrealistic assumptions and produces incorrect coefficient estimates. The advantages of a multilevel IRT model over a probit or logit model are illustrated with a Monte Carlo experiment and an example from the U.S. House. Finally, this paper provides a practical guide to fitting this model to legislative voting data.

• Nuclear Engineering and Technology
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• 제55권8호
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• pp.2844-2853
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• 2023

A sophisticated Bayesian multilevel model for estimating group bias was developed to improve the utility of the ASTM E900-15 embrittlement trend curve (ETC) to assess the conditions of nuclear power plants (NPPs). For multilevel model development, the Baseline 22 surveillance dataset was basically classified into groups based on the NPP name, product form, and notch orientation. By including the notch direction in the grouping criteria, the developed model could account for TTS differences among NPP groups with different notch orientations, which have not been considered in previous ETCs. The parameters of the multilevel model and biases of the NPP groups were calculated using the Markov Chain Monte Carlo method. As the number of data points within a group increased, the group bias approached the mean residual, resulting in reduced credible intervals of the mean, and vice versa. Even when the number of surveillance test data points was less than three, the multilevel model could estimate appropriate biases without overfitting. The model also allowed for a quantitative estimate of the changes in the bias and prediction interval that occurred as a result of adding more surveillance test data. The biases estimated through the multilevel model significantly improved the performance of E900-15.

• Genomics & Informatics
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• 제20권3호
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• pp.34.1-34.11
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• 2022

Multilevel analysis is an appropriate and powerful tool for analyzing hierarchical structure data widely applied from public health to genomic data. In practice, however, we may lose the information on multiple nesting levels in the multilevel analysis since data may fail to capture all levels of hierarchy, or the top or intermediate levels of hierarchy are ignored in the analysis. In this study, we consider a multilevel linear mixed effect model (LMM) with single imputation that can involve all data hierarchy levels in the presence of missing top or intermediate-level clusters. We evaluate and compare the performance of a multilevel LMM with single imputation with other models ignoring the data hierarchy or missing intermediate-level clusters. To this end, we applied a multilevel LMM with single imputation and other models to hierarchically structured cohort data with some intermediate levels missing and to simulated data with various cluster sizes and missing rates of intermediate-level clusters. A thorough simulation study demonstrated that an LMM with single imputation estimates fixed coefficients and variance components of a multilevel model more accurately than other models ignoring data hierarchy or missing clusters in terms of mean squared error and coverage probability. In particular, when models ignoring data hierarchy or missing clusters were applied, the variance components of random effects were overestimated. We observed similar results from the analysis of hierarchically structured cohort data.

• 응용통계연구
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• 제27권1호
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• pp.89-102
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• 2014

본 연구에서는 질병관리본부에서 매년 조사하고 있는 지역사회 건강조사 자료를 이용하여 서울시 지역을 대상으로 개인의 흡연 여부에 대한 영향 요인을 확인하고 지역간 차이를 모형에 반영시키는 다수준 로지스틱 모형을 이용하여 분석하였다. 다수준 모형에서의 적합한 분석 모형의 수준을 결정하기 위해 ICC(intraclass correlation coefficient)와 프로파일링 분석, 수준별 모형의 예측정확도를 이용하였다. 제안된 모형들의 성능을 평가하기 위해 민감도, 특이도, 정확도를 구하고 ROC curve를 작성하였다. 결과적으로 지역사회 건강조사 자료와 같이 개인과 집단 변수를 동시에 고려할 수 있다면 다양한 다수준 모형의 적용이 가능하며 활용성이 높다는 것을 알 수 있었다.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.701-718
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• 2006

In cancer microarray experiments, the experimenter or patient which is nested in each experimenter often shows quite heterogeneous error variability, which should be estimated for identifying a source of variation. Our study describes a Bayesian method which utilizes clinical information for identifying a set of DE genes for the class of subtypes as well as assesses and examines the experimenter effect and patient effect which is nested in each experimenter as a source of variation. We propose a Bayesian multilevel mixed effect model based on analysis of covariance (ANACOVA). The Bayesian multilevel mixed effect model is a combination of the multilevel mixed effect model and the Bayesian hierarchical model, which provides a flexible way of defining a suitable correlation structure among genes.

• Nuclear Engineering and Technology
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• 제50권4호
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• pp.532-541
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• 2018

Decision support systems are a key focus of research on developing control rooms to aid operators in making reliable decisions and reducing incidents caused by human errors. For this purpose, models of complex systems can be developed to diagnose causes or consequences for specific alarms. Models applied in safety systems of complex and safety-critical systems require rigorous and reliable model building and testing. Multilevel flow modeling is a qualitative and discrete method for diagnosing faults and has previously only been validated by subjective and qualitative means. To ensure reliability during operation, this work aims to synthesize a procedure to measure model performance according to diagnostic requirements. A simple procedure is proposed for validating and evaluating the concept of multilevel flow modeling. For this purpose, expert statements, dynamic process simulations, and pilot plant experiments are used for validation of simple multilevel flow modeling models of a hydrocyclone unit for oil removal from produced water.

• 대한토목학회논문집
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• 제31권3D호
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• pp.469-476
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• 2011

본 연구는 다층모형(multilevel model)을 상수원관리지역 주민지원사업 정책평가에 활용하는데 있어 유효성 및 효과를 이해하는데 연구의 목적이 있다. 정책의 대상이 위계적인(hierarchical) 특성을 지니고 있는 경우 다층모형을 이용하여 정책평가를 하는 것이 정책의 효과를 평가하고 검증하는데 있어 적절하다. 또한 이 모형은 기존 평가결과의 재평가 등에 응용될 수 있다. 상수원관리지역의 주민지원사업을 대상으로 주민만족도와 경제적 도움정도를 정책효과변수로 하여 다층모형을 적용 하였다. 그 결과 정책효과변수에 기존 평가결과 및 중요한 사업으로 여겨지던 소득증대사업 등이 직접적인 영향을 미치지 못함을 통계적으로 확인할 수 있었다. 본 연구는 다음과 같은 세 가지의 정책적 함의를 지닌다. 첫째, 정책 및 사업평가에 있어 정책의 효과를 엄밀히 파악하기 위하여 위계적 구조를 고려할 수 있는 다층모형을 이용하여야 한다. 둘째, 기관평가의 순위 도출에 있어 다층모형을 이용하여 지표에 의한 성과지표와 보완적으로 이용해야 한다. 셋째, 위계적 구조의 하나로서 공간적 위계를 고려하여 정책평가를 시행하여야 한다.

• 감성과학
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• 제19권4호
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• pp.95-110
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• 2016

본 연구의 목적은 다층자료 매개효과의 분석 방법을 제안하는 것이다. 연구내용은 다층자료 매개효과의 통계방법 탐색, 분석절차 제안, 그리고 분석의 실례 제시 등 세 가지다. 첫째, MLM (multilevel modeling)과 MSEM (multilevel structural equation modeling) 중에서 어떤 방법이 다층자료의 매개효과 분석에 유용한지 탐색하였다. MSEM은 MLM의 약점을 극복한 것으로서 유용한 다층 매개효과 분석방법이었다. 둘째, 다층자료 매개효과의 분석절차를 연구모형설정, 전제조건 검토, 모형검증, 계수검증의 4단계로 전개하였다. 셋째, 매개효과 분석의 실례에 사용된 자료는 2,695명의 초중등 학생과 88명의 학급교사로 구성되었다. 분석 실례로 2층${\rightarrow}$2층${\rightarrow}$1층과 1층${\rightarrow}$1층${\rightarrow}$2층 두 가지를 제시하였다. 2층${\rightarrow}$2층${\rightarrow}$1층과 1층${\rightarrow}$1층${\rightarrow}$2층 모형은 완전매개모형이 지지되었지만, 2층${\rightarrow}$2층${\rightarrow}$1층 모형의 매개효과 계수만 95% 신뢰구간에서 유의하였다. 분석 실례에 사용된 Mplus 프로그램은 부록에 제시하였다. 연구결과를 기초로 본 연구의 의의와 제한점, 그리고 후속연구의 방향이 논의되었다.

• Journal of Power Electronics
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• 제15권2호
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• pp.504-517
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• 2015

This paper presents a novel switching voltage model and an offset-based pulse width modulation (PWM) scheme for multilevel inverters with unbalanced DC sources. The switching voltage model under a DC voltage imbalance will be formulated in general form for multilevel neutral-point-clamped topologies. Analysis of the reference switching voltages from active and non-active switching voltage components in abc coordinates can enable voltage implementation for an unbalanced DC-source condition. Offset voltage is introduced as an indispensable variable in the switching voltage model for multilevel voltage-source inverters. The PWM performance is controlled through the design of two offset components in a subsequence. One main offset may refer to the common mode voltage, and the other offset restricts its effect on the quality of PWM control in related DC levels. The PWM quality can be improved as the switching loss is reduced in a discontinuous PWM mode by setting the local offset, which is related to the load currents. The validity of the proposed algorithm is verified by experimental results.