• Nuclear Engineering and Technology
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• v.53 no.12
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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.

• Analyses & Alternatives
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• v.5 no.1
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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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• v.55 no.8
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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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• v.20 no.3
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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.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.89-102
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• 2014

In this study, we analyze the effect on the smoking status in the Seoul Metropolitan area using a multilevel logistic model with Community Health Survey data from the Korea Centers for Disease Control and Prevention. Intraclass correlation coefficient (ICC), profiling analysis and two types of predicted value were used to determine the appropriate multilevel analysis level. Sensitivity, specificity, percentage of correctly classified observations (PCC) and ROC curve evaluated model performance. We showed the applicability for multilevel analysis allowed for the possibility that different factors contribute to within group and between group variability using survey data.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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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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• v.50 no.4
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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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3D
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• pp.469-476
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• 2011

The purposes of research is to understand the need of and the effectiveness of multilevel model to evaluate community support program in watershed areas. If the properties of policy target have hierarchical characteristics, the multilevel analysis is an adequate method to evaluate and test the effectiveness of policy. Also, the technique of multilevel modeling is extended to testing the relevance between performance appraisal and policy effectiveness. The case study of watershed region's community support program was estimated using satisfaction and economic aid level of policy target. This research has three results. First, the multilevel analysis should be used in nested data structure to estimate the effect of policy intervention. Second, the indexes of multilevel modeling should be used complementally to that of the traditional index approach. Third, the spatial hierarchical structure should be considered as the hierarchical structure in policy evaluation.

• Science of Emotion and Sensibility
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• v.19 no.4
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• pp.95-110
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• 2016

The purpose of this study was to propose a proper method for the multilevel mediation analysis, for which the hierarchical method should be utilized, then MLM (multilevel modeling) approach as a hierarchical method has been popularly utilized until MSEM (multilevel structural equation modeling) approach was not proposed. This purpose was covered by three research questions about statistical methods, analytic procedure, and real example. First, MSEM statistical method was preferred to MLM method for its estimation accuracy and analytic flexibility. Second, the four-step procedures of model building, assumption examination, model comparison, and coefficient testing were proposed for the multilevel mediation analysis. Third, the real data of 2695 students of elementary and secondary schools and 89 teachers were analyzed in the multilevel directions of $2{\rightarrow}2{\rightarrow}1$ and $1{\rightarrow}1{\rightarrow}2$. Out of these directions of $2{\rightarrow}2{\rightarrow}1$, and $1{\rightarrow}1{\rightarrow}2$ model, only the coefficient of $2{\rightarrow}2{\rightarrow}1$ model was significant at the 95% CI. Mplus programs used for the real example are attached on the Appendix. Based on the results, significance and limitations of this study, were discussed in detail.

• Journal of Power Electronics
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• v.15 no.2
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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.