• Journal of the Korean Dietetic Association
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• v.25 no.2
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• pp.142-155
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• 2019

The aim of this study was to construct and test a structural equation model for the risk factors of metabolic syndrome in Korean adults. The structural equation model hypothesizes that eating alone and feeling depressed is a risk factor for metabolic syndrome. The data of this study were obtained from the Sixth Korea National Health and Nutrition Examination Survey which was cross-sectional data from the representative national survey. A total of 4,013 subjects replied to the survey item of lifestyle and completed the physical examinations among adults aged 19 years or older in South Korea was in 2015. The structural model in this study was composed of four latent variables: eating alone, depression, negative health behavior, and metabolic syndrome. Two variables, the rate of eating alone and depression, were exogenous variables. Negative health behavior was both a mediating variable and endogenous variable, and metabolic syndrome was the final endogenous variable. The data were analyzed using the Maximum Likelihood method and bootstrapping. The structural model was appropriate for the data based on the model fit indices. The results of this study can be summarized as follows: Eating alone is a direct risk factor of metabolic syndrome in Korean women. Depression can mediate metabolic syndrome through negative health behaviors. Negative health behavior had a direct impact on metabolic syndrome in both men and women. This study may be a guideline for interventions and strategies to reduce the incidence of metabolic syndrome in Korean adults.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.999-1006
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• 2020

This study provides an understanding of the role of job satisfaction as a mediator of compensation and workplace environments for the organizational commitment of employees in the public sector. This study used a structural model using path analysis. The population and sample in this study were all employees at the Population and Civil Registry Office of one of the districts in Indonesia. The sampling technique used was total sampling, due to the considerably smaller amount of the sample size. This study found that compensation and workplace environment could explain job satisfaction variables with a 93.8% confidence level and simultaneously compensation, workplace environment, and job satisfaction that could explain organizational commitment with a variable of 97.4%. This findings also shows that the manifest bonus variable on the latent compensation variable is one of the main indicators that needs to improve to increase job satisfaction and organizational commitment. One of the important things which needs to be done is to increase compensation. The first thing which needs to be done is to increase the bonus. Furthermore, to improve the quality of the workplace environment, facilities, and infrastructure such as stable internet connections, computer specifications are the important criteria that must be met.

• Asian Journal for Public Opinion Research
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• v.10 no.2
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• pp.123-146
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• 2022

This study aims to identify the effect of the relationship between human resources management (HRM) and total quality management (TQM) on improving employee performance. Several previous qualitative studies have stated that TQM and HRM are separate methods. This article describes a new method using a quantitative approach. This research is needed to fill the gap in the literature by empirically analyzing the relationship between HRM, TQM practices, and organizational performance. Data was collected quantitatively from 100 employees in Indonesia through questionnaires and online survey methods. The data collected were analyzed using structural equation modeling (SEM) with the Lisrel 8.5 system. TQM-oriented HRM is operationalized as a second-order latent variable measured by four factors (training, empowerment, teamwork, compensation). The findings support the validity of the TQM-oriented HRM model as a hierarchical, second-order latent construct and show a strong relationship with employee performance. The results of this study are different from previous studies, which showed that TQM and HRM are separate methods. The results of our research provide an academic and practical overview that TQM-oriented HRM can be used to help organizations build platforms for human resources policies aimed at improving employee performance.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.951-961
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• 2011

In this paper, we propose a Bayesian inference using the Markov Chain Monte Carlo(MCMC) method for the zero inflated negative binomial(ZINB) regression model. The proposed model allows the regression model for zero inflation probability as well as the regression model for the mean of the dependent variable. This extends the work of Jang et al. (2010) to the fully defiend ZINB regression model. In addition, we apply the proposed method to a real data example, and compare the efficiency with the zero inflated Poisson model using the DIC. Since the DIC of the ZINB is smaller than that of the ZIP, the ZINB model shows superior performance over the ZIP model in zero inflated count data with overdispersion.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.385-395
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• 2020

We present a new maximum likelihood approach to estimate demographic history using genomic data sampled from two populations. A demographic model such as an isolation-with-migration (IM) model explains the genetic divergence of two populations split away from their common ancestral population. The standard probability model for an IM model contains a latent variable called genealogy that represents gene-specific evolutionary paths and links the genetic data to the IM model. Under an IM model, a genealogy consists of two kinds of evolutionary paths of genetic data: vertical inheritance paths (coalescent events) through generations and horizontal paths (migration events) between populations. The computational complexity of the IM model inference is one of the major limitations to analyze genomic data. We propose a fast maximum likelihood approach to estimate IM models from genomic data. The first step analyzes genomic data and maximizes the likelihood of a coalescent tree that contains vertical paths of genealogy. The second step analyzes the estimated coalescent trees and finds the parameter values of an IM model, which maximizes the distribution of the coalescent trees after taking account of possible migration events. We evaluate the performance of the new method by analyses of simulated data and genomic data from two subspecies of common chimpanzees in Africa.

• Journal of Korea Foundry Society
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• v.20 no.5
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• pp.290-299
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• 2000

A three-dimensional model was developed in order to simulate heat and fluid flow of a continuous casting billet. The model was coded with the general-purpose CFD program FIDAP, using the finite element method. The present model consists of 2 individual calculation schemes, named model 1 and model 2. Mold region only was calculated to check the pouring stream through submerged nozzle with model 1. Entire region, which consists of mold, secondary cooling, radiation cooling was calculated to predict crater end position, temperature profile and solid shell profile(model 2). Standard $k-{\bullet}\hat{A}$ turbulence model has been applied to simulate the turbulent flow induced by submerged nozzle. Enthalpy method was adopted for the latent heat of solidification. Fluid flow in mushy zone was treated using variable viscosity approach. The more casting speed and superheat increased, the more metallurgical length increased. The shell thickness at the mold exit is proved to be mainly controlled by superheat by the present simulation. It may be concluded that the present model can be successfully applied far the prediction of heat and fluid flow behavior in the continuous casting process.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.57-71
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• 2002

We introduce three types of exponential survival models, such as simple model, change-point model and finite mixture model in this paper. Among these models, in order to choose the best model, the model choice method is proposed using Gelfand and Ghosh(1998)'s idea. Then to avoid the computational difficulties, data augmentation method (Tanner and Wong, 1987) and Gibbs sampler (Gelfand and Smith, 1990) are employed. Our methodology is applied to both simulated data and Stangl (1991)'s On-impramint Hydrochloride data.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.270-283
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• 2012

With the advent of multi-channel TV, IPTV and smart TV services, excessive amounts of TV program contents become available at users' sides, which makes it very difficult for TV viewers to easily find and consume their preferred TV programs. Therefore, the service of automatic TV recommendation is an important issue for TV users for future intelligent TV services, which allows to improve access to their preferred TV contents. In this paper, we present a recommendation model based on statistical machine learning using a collaborative filtering concept by taking in account both public and personal preferences on TV program contents. For this, users' preference on TV programs is modeled as a latent topic variable using LDA (Latent Dirichlet Allocation) which is recently applied in various application domains. To apply LDA for TV recommendation appropriately, TV viewers's interested topics is regarded as latent topics in LDA, and asymmetric Dirichlet distribution is applied on the LDA which can reveal the diversity of the TV viewers' interests on topics based on the analysis of the real TV usage history data. The experimental results show that the proposed LDA based TV recommendation method yields average 66.5% with top 5 ranked TV programs in weekly recommendation, average 77.9% precision in bimonthly recommendation with top 5 ranked TV programs for the TV usage history data of similar taste user groups.

• Journal of the Korean Statistical Society
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• v.32 no.2
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• pp.121-150
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• 2003

The National Health Interview Survey (NHIS) is one of the surveys used to assess the health status of the US population. One indicator of the nation's health is the total number of doctor visits made by the household members in the past year, There is a substantial nonresponse among the sampled households, and the main issue we address here is that the nonrespones mechanism should not be ignored because respondents and nonrespondents differ. It is standard practice to summarize the number of doctor visits by the binary variable of no doctor visit versus at least one doctor visit by a household for each of the fifty states and the District of Columbia. We consider a nonignorable nonresponse model that expresses uncertainty about ignorability through the ratio of odds of a household doctor visit among respondents to the odds of doctor visit among all households. This is a hierarchical model in which a nonignorable nonresponse model is centered on an ignorable nonresponse model. Another feature of this model is that it permits us to "borrow strength" across states as in small area estimation; this helps because some of the parameters are weakly identified. However, for simplicity we assume that the hyperparameters are fixed but unknown, and these hyperparameters are estimated by the EM algorithm; thereby making our method Bayes empirical Bayes. Our main result is that for some of the states the nonresponse mechanism can be considered non-ignorable, and that 95% credible intervals of the probability of a household doctor visit and the probability that a household responds shed important light on the NHIS.

• Research in Community and Public Health Nursing
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• v.12 no.3
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• pp.557-569
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• 2001

The purpose of the study was to test the Pender's New Health Promotion Model in order to explain and predict female workers' health promotion behavior at manufacturing plants by using latent variable structural equation model. The data were collected from 280 female workers at 8 electronic factories located at Seoul. Kyunggi. and Incheon using a structured questionnaire through interview and self-report. LISREL was used to test the model. The results are as follows: 8 out of 15 paths of the modified one from the hypothetical model of Health Promotion were statistically significant and the total variance was 40%. The relationship between the previous health behavior and the cognitive emotional factor, and the interpersonal factor. and the situational factor each. and the relationship between perceived health status and interpersonal factor, and health promotion behavior each among gamma paths were unidirectional. On the beta paths. the relationship between the interpersonal factor and the cognitive emotional factor was bi-directional: the relationships amongst the interpersonal factor and the commitment to action, and the health promotion behavior were unidirectional. But the commitment to action was not a significant mediating factor to the health promotion behavior. Pender's New Model is considered good to explain and predict the female workers' health promotion behavior. The interpersonal factor should be considered in occupational nursing practice. But the concepts of situation and commitment to action should be further validated and measured.