• Journal of Families and Better Life
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• v.26 no.6
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• pp.83-94
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• 2008

The purpose of this study was to identify what factors are associated with marital satisfaction of the foreign wives who live in small town. Data for the study were based on a snowball sampling of 101 foreign wives who married to Korean husbands and had residence in Changwon and Masan city, Gyeongnam, Korea. The respondents reported higher levels of marital satisfaction and lower levels of marital hardship. To identify the variables affecting marital satisfaction, a regression analysis was conducted. Independent variables included age difference, educational difference, monthly income, conformity of information about husband, marital hardship, relationship with parents-in-law and taking class with husband. While conformity of information about husband, marital hardship and taking class with husband had strong effects on marital satisfaction of the foreign wives, age difference, educational difference, monthly income and relationship with parents-in-law didn't show the significant effects on the marital satisfaction of the foreign wives.

• Journal of the Korean Home Economics Association
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• v.49 no.9
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• pp.59-72
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• 2011

This study examined whether there are underlying latent classes of growth trajectories of maternal depression in the Korean population. Data from the first phase of the Panel Study of Korean Children (PSKC) of the Institute of Child Care and Education (KICCE) were used for this study. The final sample of participants included 1,471 mothers, who completed three interviews: at birth, at one month, and at four months. A two-class model consisting of depression (12.3%) and non-depression (87.7%) was considered the best-fitting solution using Mplus 3.13. The changes in postnatal depression in the Korean population within four weeks after childbirth, which is the period of "postpartum onset", seem to be important. Logistic regression analysis showed that duration of breast-feeding and planned pregnancy effects were significantly associated with trajectory class membership.

• Journal of Korean Society for Quality Management
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• v.22 no.2
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• pp.89-97
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• 1994

We consider the linear calibration model: $y_1={\alpha}+{\beta}x_i+{\sigma}{\varepsilon}_i$, i = 1, ${\cdots}$, n, $y={\alpha}+{\beta}x+{\sigma}{\varepsilon}$ where ($y_1$, ${\cdots}$, $y_n$, y) stands for an observation vector, {$x_i$} fixed design vector, (${\alpha}$, ${\beta}$) vector of regression parameters, x unknown true value of interest and {${\varepsilon}_i$}, ${\varepsilon}$ are mutually uncorrelated measurement errors with zero mean and unit variance but otherwise unknown distributions. On the basis of simple small-sample low-noise approximation, we introduce a new method of comparing the mean squared errors of the various competing estimators of the true value x for finite sample size n. Then we show that a class of estimators including the classical and the inverse estimators are consistent and first-order efficient within the class of all regular consistent estimators irrespective of type of measurement errors.

• The Korean Journal of Health Service Management
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• v.10 no.1
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• pp.117-129
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• 2016

Objectives : This study investigated the prevalence of unmet health care needs among Korean adults and related factors. Methods : The study participants were adults over the age of 20 mental health experience from the Korea Health Panel in 2012(n=4,730). Statistical analysis methods used in this study were the ${\chi}^2$-test, Logistic Regression Analysis and other basic statistics such frequency-and percentage using SPSS version 22.0. Results : (1)Significant variables of stress: Factors were age, economic activity, subjective health status, and activity limitation. (2)Significant variables of depression: Factors were age, income class(low) and activity limitation. (3)Significant variables of suicidal impulse: Factors were age, chronic diseases, income class, and activity limitation. Conclusions : Stress, depression, and suicidal impulse can be unmet medical factors; therefore improvement measures and mental health counseling programs in response to suicide impulses, should be developed. Thus there is a need for a health sciences approach.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.565-574
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• 2004

Classification tree algorithms including as CART by Brieman et al.(1984) in some aspects, recursively partition the data space with the aim of making the distribution of the class variable as pure as within each partition and consist of several steps. SUPPORT(smoothed and unsmoothed piecewise-polynomial regression trees) method of Chaudhuri et al(1994), a weighted averaging technique is used to combine piecewise polynomial fits into a smooth one. We focus on applying SUPPORT to a binary class variable. Logistic model is considered in the caculation techniques and the results are shown good classification rates compared with other methods as CART, QUEST, and CHAID.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.175-189
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• 2019

The problem of the estimation of quantitative sensitive variable using the item sum technique (IST) on successive occasions has been discussed. IST difference, IST regression, and IST general class of estimators have been proposed to estimate quantitative sensitive variable at the current occasion in two occasion successive sampling. The proposed new estimators have been elaborated under Trappmann et al. (Journal of Survey Statistics and Methodology, 2, 58-77, 2014) as well as Perri et al. (Biometrical Journal, 60, 155-173, 2018) allocation designs to allocate long list and short list samples of IST. The properties of all proposed estimators have been derived including optimum replacement policy. The proposed estimators have been mutually compared under the above mentioned allocation designs. The comparison has also been conducted with a direct method. Numerical applications through empirical as well as simplistic simulation has been used to show how the illustrated IST on successive occasions may venture in practical situations.

• The Journal of Korean Society for School & Community Health Education
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• v.23 no.2
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• pp.65-76
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• 2022

Objectives: The purpose of this study is to investigate the factors that affect the smoking type among those who have used regular cigarettes, liquid or cigarette-type e-cigarettes. Methods: The subjects of analysis were 6,081 people who had smoked regular cigarettes or e-cigarettes. For data analysis, SPSS ver.25.0 statistical package program was used. Multinomial logistic regression analysis was performed to find out the factors affecting smoking type. Results: Factors affecting the experience of using e-cigarettes compared to regular cigarette smoking are gender and class. Academic performance, living with family members, drinking experience, and secondhand smoke in school. The factors influencing dual use compared to regular cigarette smoking were gender, class, academic performance, economic status, living with family, drinking experience, and experience of secondhand smoke in school. Smoking cessation attempts had an effect on dual use compared to regular cigarette smoking. Conclusion: Smoking cessation experience had a greater effect on e-cigarette use than regular cigarette smoking.

• Communications for Statistical Applications and Methods
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• v.31 no.4
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• pp.393-408
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• 2024

The sparse linear discriminant analysis can be incorporated into the penalized linear regression framework, but most studies have been limited to specific convex penalties, including the least absolute selection and shrinkage operator and its variants. Within this framework, concave penalties can serve as natural counterparts of the convex penalties. Implementing the concave penalized direction vector of discrimination appears to be straightforward, but developing its theoretical properties remains challenging. In this paper, we explore a class of concave penalties that covers the smoothly clipped absolute deviation and minimax concave penalties as examples. We prove that employing concave penalties guarantees an oracle property uniformly within this penalty class, even for high-dimensional samples. Here, the oracle property implies that an ideal direction vector of discrimination can be exactly recovered through concave penalized least squares estimation. Numerical studies confirm that the theoretical results hold with finite samples.

• Journal of Oral Medicine and Pain
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• v.34 no.3
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• pp.277-294
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• 2009

The purpose of this study was to evaluate the soft tissue changes of class II adolescents and adults in respect to extraction or nonextraction. The study included 68 patients from Wonkwang Dental Hospital were categorized to adolescent extraction group, adolescent nonextraction group, adult extraction group, adult nonextraction group. Cephalometric tracing of each patient was done to compare pretreatment and posttreatment of each group, to compare the changes between groups. And among the variables that showed significancy, correlation analysis and simple linear regression were done. The results were as follows. 1. In both adolescents and adults after extraction treatment, nasolabial angle significantly increased and in both subjects after non extraction treatment, nasolabial angle significantly decreased. 2. In extraction subjects, there were positive correlation between the amount of treatment changes of vertical-U1 and E line-upper lip, the changes of vertical-L1 and E line-lower lip, the changes of vertical-L1 and vertical-Li. 3. In extraction subjects, simple regression equations of E line-upper lip, E line-lower lip, Li were calculated by regression analysis. According to the results above, it could be considered that the effect of the extraction or nonextraction treatment was greater than the effect of growth.

• Journal of the Korean association of regional geographers
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• v.22 no.1
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• pp.251-266
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• 2016

Intelligent interpolation methods such as dasymetric mapping are considered to be the best way to disaggregate zone-based population data by observing and utilizing the internal variation within each source zone. This research reviews the advantages and problems of the dasymetric mapping method, and presents a geographically weighted regression (GWR) based method to take into consideration the spatial heterogeneity of population density - land cover relationship. The locally adaptive intelligent interpolation method is able to make use of readily available ancillary information in the public domain without the need for additional data processing. In the case study, we use the preclassified National Land Cover Dataset 2011 to test the performance of the proposed method (i.e. the GWR-based multi-class dasymetric method) compared to four other popular population estimation methods (i.e. areal weighting interpolation, pycnophylactic interpolation, binary dasymetric method, and globally fitted ordinary least squares (OLS) based multi-class dasymetric method). The GWR-based multi-class dasymetric method outperforms all other methods. It is attributed to the fact that spatial heterogeneity is accounted for in the process of determining density parameters for land cover classes.