• Child Health Nursing Research
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• v.26 no.1
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• pp.90-97
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• 2020

Purpose: The purpose of this study was to investigate the effects of a breastfeeding support program (BSP) on the prevalence of exclusive breastfeeding and growth in late-preterm infants. Methods: A quasi-experimental study was conducted. The participants were 40 late preterm infants (LPIs), of whom 20 were assigned to the experimental group and 20 to the control group. For the mothers in the experimental group, a BSP was provided prior to the LPIs' discharge and reinforced once a week for 4 weeks. Information on the feeding type was collected by observation and the LPIs' body weight was measured. Results: There were significant differences in feeding type by group and time. Exclusive breastfeeding was 5.18 times more common in the experimental group than in the control group (odds ratio=5.18, 95% confidence interval=1.11~16.70). However, weekly weight gain did not show a significant relationship with group and time (F=0.40, p=.712). Conclusion: The BSP was helpful for increasing the rate of exclusive breastfeeding in LPIs. Furthermore, the LPIs in the experimental group, which had a higher likelihood of being exclusively breastfed, showed an equivalent amount of weight gain as the LPIs in the control group, in which infants were more likely to be formula-fed.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.171-180
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• 2016

Basel committee suggests using Value-at-Risk (VaR) and expected shortfall (ES) as a measurement for market risk. Various estimation methods of VaR and ES have been studied in the literature. This paper compares semi-parametric methods, such as conditional autoregressive value at risk (CAViaR) and conditional autoregressive expectile (CARE) methods, and a Gaussian quasi-maximum likelihood estimator (QMLE)-based method through back-testing methods. We use unconditional coverage (UC) and conditional coverage (CC) tests for VaR, and a bootstrap test for ES to check the adequacy. A real data analysis is conducted for S&P 500 index and Hyundai Motor Co. stock price index data sets.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.369-385
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• 1999

For the growth curve model with arbitrary covariance structure, known as unstructured covariance matrix, the problems of detecting outliers are discussed in this paper. In order to detect outliers in the growth curve model, the likelihood ratio testing statistics in mean shift model is established and its distribution is derived. After we detected outliers in growth curve model, we test homo and/or hetero-geneous covariance matrices using PSR Quasi-Bayes Criterion. For illustration, one numerical example is discussed, which compares between before and after outlier deleting.

• Journal of Korean Society on Water Environment
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• v.23 no.2
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• pp.251-259
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• 2007

In this study three log-transformed linear regression models are compared with the focus of bias correction problem. The models are the traditional simple linear regression estimator (SL), the quasi maximum likelihood estimator (QMLE) and the minimum variance unbiased estimator (MVUE). Using such models, suspended solid loads can be estimated using the discharge - suspended solid data set that has been measured by NIER Nakdong River Water Environment Laboratory. As a result, SL shows negative bias for most values of the measured discharge range. QMLE is nearly unbiased for moderate values of the measured discharge range, but shows increasingly positive bias for either large or small value of the measured discharge range. MVUE is unbiased. It is also analyzed how the estimated regression coefficient and exponent are distributed along Nakdong river main stream.

• Child Health Nursing Research
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• v.27 no.4
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• pp.377-384
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• 2021

Purpose: This study examined the effects of a breastfeeding coaching program for mothers on growth and neonatal jaundice in late preterm infants (LPIs). Methods: This was a quasi-experimental study (non-randomized intervention) with a time-series design. The study was conducted among 40 LPIs who were admitted to the neonatal intensive care unit of a university hospital in Daegu, South Korea. In the order of admission, the first 21 infants were assigned to the experimental group, and 19 were assigned to the control group. The intervention program consisted of home- based and web-based practical breastfeeding support education for mothers across a total of 5 sessions. Infant growth was measured using body weight, length, and head circumference, and neonatal jaundice was assessed using transcutaneous bilirubin levels. Results: The likelihood of breastfeeding for infants in the experimental group at 4 weeks after discharge was the same as on the day of discharge, whereas it steadily decreased in the control group. There were significant differences in head circumference between the groups. However, weight, length, and transcutaneous bilirubin levels did not show a significant group-time interaction. Conclusion: A formal breastfeeding coaching program should be considered in clinical settings and at home within the first few weeks postpartum.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.539-553
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• 2017

We propose a multi-state model to analyze semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the three states of the illness-death model: healthy, disease, and dead. The 'diseased' state can be considered as the intermediate event. Two more states are added into the illness-death model to incorporate the missing events, which are caused by a loss of follow-up before the end of a study. One of them is a state of the lost-to-follow-up (LTF), and the other is an unobservable state that represents an intermediate event experienced after the occurrence of LTF. Given covariates, we employ the Lin and Ying additive hazards model with log-normal frailty and construct a conditional likelihood to estimate transition intensities between states in the multi-state model. A marginalization of the full likelihood is completed using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through an iterative quasi-Newton algorithm. Simulation studies are performed to investigate the finite-sample performance of the proposed estimation method in terms of empirical coverage probability of true regression parameters. Our proposed method is also illustrated with a dataset adapted from Helmer et al. (2001).

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.269-278
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• 2011

There are many researches that have considered the distribution functions and appropriate covariates corresponding to the scores in order to improve the accuracy of a diagnostic test, including the ROC curve that is represented with the relations of the sensitivity and the specificity. The ROC analysis was used by the regression model including some covariates under the assumptions that its distribution function is known or estimable. In this work, we consider a general situation that both the distribution function and the elects of covariates are unknown. For the ROC analysis, the mixtures of normal distributions are used to estimate the distribution function fitted to the credit evaluation data that is consisted of the score random variable and two sub-populations of parameters. The AUC measure is explored to compare with the nonparametric and empirical ROC curve. We conclude that the method using normal mixtures is fitted to the classical one better than other methods.

• Journal of Korean Biological Nursing Science
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• v.22 no.1
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• pp.1-10
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• 2020

Purpose: Diabetes Mellitus (DM) is a leading cause of death with a prevalence rate of 12.4% in South Korea. Self-management is crucial for patients with DM, because many studies have reported that self-management intervention based on the Health Belief Model (HBM) is effective. The purpose of this study was to investigate the current state of HBM based intervention studies and the components and effects of the theories used in the study for diabetes patients. Methods: A systematic review was conducted using the Pubmed, Cochrane Library and Embase databases from January 2009 to May 2019. We reviewed characteristics of intervention based on the HBM in randomized controlled clinical trials (RCTs), quasi-experimental study intervention. Results: Eight studies published in English between 2009 and 2019 were included in this review. The key components of the health behavior promotion program applied to the DM patients were perceived susceptibility, perceived severity, perceived benefits, perceived barriers and self-efficacy. The intervention based on these components has reported to significantly increase the health behavior change, likelihood of taking health action and improve physiological indicators (HbA1c, and fasting blood sugar etc.). Conclusion: This study highlighted the importance of intervention programs based on the HBM for DM patients.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.268-272
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• 2010

The research presented here represents a collaborative effort with the SFWMD on developing scenarios for future climate for the SFWMD area. The project focuses on developing methodology for simulating precipitation representing both natural quasi-oscillatory modes of variability in these climate variables and also the secular trends projected by the IPCC scenarios that are publicly available. This study specifically provides the results for precipitation modeling. The starting point for the modeling was the work of Tebaldi et al that is considered one of the benchmarks for bias correction and model combination in this context. This model was extended in the framework of a Hierarchical Bayesian Model (HBM) to formally and simultaneously consider biases between the models and observations over the historical period and trends in the observations and models out to the end of the 21st century in line with the different ensemble model simulations from the IPCC scenarios. The low frequency variability is modeled using the previously developed Wavelet Autoregressive Model (WARM), with a correction to preserve the variance associated with the full series from the HBM projections. The assumption here is that there is no useful information in the IPCC models as to the change in the low frequency variability of the regional, seasonal precipitation. This assumption is based on a preliminary analysis of these models historical and future output. Thus, preserving the low frequency structure from the historical series into the future emerges as a pragmatic goal. We find that there are significant biases between the observations and the base case scenarios for precipitation. The biases vary across models, and are shrunk using posterior maximum likelihood to allow some models to depart from the central tendency while allowing others to cluster and reduce biases by averaging. The projected changes in the future precipitation are small compared to the bias between model base run and observations and also relative to the inter-annual and decadal variability in the precipitation.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.199-226
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• 2009

We examine the intertemporal relation between risk and return in the Korean stock market and foreign exchange market based on the two factor ICAPM framework. The standard GARCH model and the GJR(1993) model are employed to estimate conditional variances of the stock returns and foreign exchange rates. The covariance between the rates of stock returns and changes in the exchange rates are estimated by the constant conditional correlation model of Bollerslev(1990) and the dynamic conditional correlation model of Engle(2002). The multivariate GARCH in mean model and quasi-maximum likelihood estimation method, consequently, are applied to investigate riskreturn relation jointly. We find that the estimated coefficient of relative risk aversion is negative and statistically significant in the post-financial crisis sample period in the Korean stock market. We also show that the expected stock returns are negatively related to the dynamic covariance with foreign exchange rates. Both estimated parameters of conditional variance and covariance in the foreign exchange market, however, are not statistically significant. The GJR model is better than the standard GARCH model to estimate the conditional variances. In addition, the dynamic conditional correlation model has higher explanatory power than the constant correlation model. The empirical results of this study suggest following two points to investors and risk managers in hedging and diversifying strategies for their portfolios in the Korean stock market: first, the variability of foreign exchange rates should be considered, and second, time-varying correlation between stock returns and changes in foreign exchange rates supposed to be considered.