• 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.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.

• 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.

• 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.

• The Journal of Industrial Distribution & Business
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• v.10 no.6
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• pp.7-17
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• 2019

Purpose - The purpose of this study is to explore how advertising for multilevel marketing brands affect the salesperson's activity including customer-salesperson interactivity, work attitude, and perceived and actual performance after the campaign. Research Design, data, and methodology - This study collects experimental data, survey data, and actual sales data and applies statistical analyses such as factor analysis, t-tests, and a structural equation model. Results - The results show that advertising campaign can enhance a salesperson's selling activities and provide wide managerial implications to a multilevel marketing firm by filling the gaps for the field of advertising research. Conclusions - Managerial implications include: i) multilevel marketing firms should consider advertising campaigns as a means of changing customer responses because advertising plays a significant role in increasing familiarity with, and knowledge of, attitudes toward the brand, which also helps salespeople interact with customers; ii) multilevel marketing firms should consider brand advertising as a means to support the sales activities of salespeople including sales effectiveness, work attitudes, and perceived performance, and iii) multilevel marketing firms should consider brand advertising as a means to enhance a salesperson's pride and motivation for selling their brand, which will lead to improved sales performances.

• Korean Journal of Child Studies
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• v.27 no.3
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• pp.169-187
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• 2006

A hierarchical linear model(HLM) provides advantages over existing traditional statistical methods (e.g., ordinary least squares regression, repeated measures analysis of variance, etc.) for analyzing multilevel/longitudinal data or diary methods. HLM can gauge a more precise estimation of lower-level effects within higher-level units, as well as describe each individual's growth trajectory across time with improved estimation. This article 1) provides scholars who study children and families with an overview of HLM (i.e., statistical assumptions, advantages/disadvantages, etc.), 2) provides an empirical study to illustrate the application of HLM, and 3) discusses the application of HLM to the study of children and families. In addition, this article provided useful information on available articles and websites to enhance the reader's understanding of HLM.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.173-184
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• 2018

In medical or public health research, it is common to encounter clustered or longitudinal count data that exhibit excess zeros. For example, health care utilization data often have a multi-modal distribution with excess zeroes as well as a multilevel structure where patients are nested within physicians and hospitals. To analyze this type of data, zero-inflated count models with mixed effects have been developed where a count response variable is assumed to be distributed as a mixture of a Poisson or negative binomial and a distribution with a point mass of zeros that include random effects. However, no study has considered a situation where data are also censored due to the finite nature of the observation period or follow-up. In this paper, we present a weighted version of zero-inflated Poisson model with random effects accounting for variable individual follow-up times. We suggested two different types of weight function. The performance of the proposed model is evaluated and compared to a standard zero-inflated mixed model through simulation studies. This approach is then applied to Medicaid data analysis.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.923-932
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• 2008

We are likely to face complex multivariate data which can be characterized by having a non-trivial correlation structure. For instance, omitted covariates may simultaneously affect more than one count in clustered data; hence, the modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference. A standard way to insert dependence among counts is to assume that they share some common unobservable variables. For this assumption, we fitted correlated random effect models considering multilevel model. Estimation was carried out by adopting the semiparametric approach through a finite mixture EM algorithm without parametric assumptions upon the random coefficients distribution.

• Journal of The Korean Astronomical Society
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• v.23 no.1
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• pp.71-82
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• 1990

Theoretical calculations of the combined radiative transfer and statistical equilibrium equation including the charge-particle conservations have been earned out for a multilevel hydrogen atom in quiescent prominences. Cool and dense models show the steep changes of population and radiation field in the vicinity of the surface, while these physical quantities remain unchanged for models with temperature of 7,300K, regardless of total densities. Ionization rate of hydrogen atom related with metallic line formation varies in considerable amounts from the surface to the center of model prominences cooler than 6,300K. However, such cool models cannot release enough hydrogen line emissions to explain observed intensities. Prominence models with a temperature higher than 8,000K can yield the centrally reversed Lyman line profiles confirmed by satellite EUV observations. We find that queiscent prominence with a density between $2{\times}10^{11}$ and $10^{12}cm^{-3}$ should be in temperature range between 6,300K and 8,300K, in order to explain consistently observed H alpha, beta line emissions and $n_p/n_l$ ratio.

• Applied Chemistry for Engineering
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• v.33 no.4
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• pp.402-407
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• 2022

Statistical design of experiments was used to optimize the MOF-5 synthesis process. A mixture design was employed to optimize precursor concentration. The optimal composition of three chemical materials, terephthalic acid, zinc acetate dihydrate, and N,N-dimethylformamide for MOF-5 synthesis was determined by extreme vertices design methods as follows; 1 mol : 2.7 mol : 40 mol. A multilevel factorial design was selected to screen the significance of synthesis reaction conditions such as temperature, time, and stirring speed. Statistical analysis results suggested excluding stirring speed from further investigation. Using a central composition design, the synthesis time and temperature were optimized. The quadratic model equation was derived from 13 synthesis experiments. The model predicted that MOF-5 synthesized at 119 ℃ for 10.4 h had the highest crystallinity.