• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.483-488
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• 2014

Aim: This study employed public use National Health and Nutrition Examination Survey (NHANES III) data to investigate the association between urinary cadmium (UDPSI) and all cause, all cancer and prostate cancer mortalities in men. Patients and Methods: NHANES III household adult, laboratory and mortality data were merged. The sampling weight used was WTPFEX6, with SDPPSU6 applied for the probability sampling unit and SDPSTRA6 to designate the strata for the survey analysis. Results: For prostate cancer death, the significant univariates were UDPSI, age, weight, and drinking. Under multivariate logistic regression, the significant covariates were age and weight. For all cause mortality in men, the significant covariates were UDPSI, age, and poverty income ratio. For all cancer mortality in men, the significant covariates were UDPSI, age, black and Mexican race. Conclusions: UDPSI was a predictor of all cause and all cancer mortalities in men as well as prostate cancer mortality.

• Journal of Families and Better Life
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• v.30 no.4
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• pp.13-24
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• 2012

This study attempts to examine the effect of family friendly work policies on the work-family balance of employed married women with young children. While previous research has investigated the effects of family friendly work policies, the effects has often been confounded with the effects of other covariates such as worker's and workplace's characteristics. In this study, we try to distinguish the effects of the family friendly work policies from those of other covariates. We draw a sample of 131 employed married women with children under age 12 from the $2^{nd}$ National Korean Family Survey. We compare the level of work-famiy balance of the women by the type of workplace: public sector, large enterprise, medium enterprise, and small enterprise. The results of this study show that some of the differences in the work-family balance of the women working in the different type of workplace can be attributed to socio-demographic background of the women and the work characteristics of workplace. There is, however, an effect of family friendly policies on the work-family balance between those who work in public sector and in medium enterprise after controlling the effects of the covariates.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.17-23
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• 2005

In this paper we consider the well-known semiparametric proportional hazards models for survival analysis. These models are usually used with few covariates and many observations (subjects). But, for a typical setting of gene expression data from DNA microarray, we need to consider the case where the number of covariates p exceeds the number of samples n. For a given vector of response values which are times to event (death or censored times) and p gene expressions(covariates), we address the issue of how to reduce the dimension by selecting the significant genes. This approach enables us to estimate the survival curve when n ${\ll}$p. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional flexibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in effect works as a penalty To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology to diffuse large B-cell lymphoma (DLBCL) complementary DNA (cDNA) data and Breast Carcinomas data.

• Journal of Korean society of Dental Hygiene
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• v.21 no.5
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• pp.575-583
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• 2021

Objectives: This study aimed to investigate the relationship between sleep quality and periodontal disease-related symptoms among Korean adolescents. Methods: This cross-sectional study was based on the 16th Korea Youth Risk Behavior Web-Based Survey (2020). A complex sample logistic regression was performed to identify the relationship between sleep quality and periodontal disease-related symptoms after adjusting for all covariates. Results: In model II, to estimate the adjusted odds ratio (AOR) for all covariates, students who answered "not at all sufficient", indicating sleep quality, were at higher risk of experiencing periodontal disease-related symptoms than those who answered "completely sufficient" (AOR=1.58). As a result of subgroup analysis, for estimating the AOR adjusted for all covariates in boys, students who answered "not at all sufficient", indicating sleep quality, were at a higher risk of experiencing periodontal disease-related symptoms than those who answered "completely sufficient" (AOR=1.68). In girls, students who answered "not at all sufficient", indicating sleep quality, were at a higher risk of experiencing periodontal disease-related symptoms than those who answered "completely sufficient" (AOR=1.43). Conclusions: It is necessary to formulate health policies that can promote optimal sleeping habits and oral health behaviors among Korean adolescents.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.6
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• pp.641-652
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• 2012

This study took the conceptual framework of acculturation styles into the empirical investigation of international students in Korea. This research identifies the differences in acculturation styles, the characteristics of each segment, the effect of acculturation styles on clothing involvement (clothing involvement and risk probability), and the effect of cultural orientation values (individualism and collectivism) as covariates. The participants were international students attending a university located in Seoul. Data from 153 international students were used for statistical analysis. Respondents were grouped into four acculturation styles (integration, assimilation, separation, and marginalization). The assimilation group had the highest mean score of clothing interest. Cultural orientation values showed a significant covariate effect. With individualism as covariates, the main effect of acculturation styles on clothing interest was significant. In clothing product evaluation criteria, the integration group regarded design, fit and trend as most important. The marginalization group showed a mean score that was significantly lower in brand preference and satisfaction; however, the assimilation group had a mean score that was significantly higher.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.751-759
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• 2008

Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.179-182
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• 2003

This study describes a new graphical method for assessing and characterizing effect modification by a matching covariate in matched case-control studies. This method to understand effect modification is based on a semiparametric model using a varying coefficient model. The method allows for nonparametric relationships between effect modification and other covariates, or can be useful in suggesting parametric models. This method can be applied to examining effect modification by any ordered categorical or continuous covariates for which cases have been matched with controls. The method applies to effect modification when causality might be reasonably assumed. An example from veterinary medicine is used to demonstrate our approach. The simulation results show that this method, when based on linear, quadratic and nonparametric effect modification, can be more powerful than both a parametric multiplicative model fit and a fully nonparametric generalized additive model fit.

• Journal of the Korean Statistical Society
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• v.25 no.4
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• pp.567-576
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• 1996

The multiprocess dynamic survival model is proposed for the application of the regression model on the analysis of survival data with time-varying effects of covariates : where the survival data consists of numbers of deaths at certain time-points. The algorithm for the recursive estimation of a time-varying parameter vector is suggested. Also the algorithm of forecasting of numbers of deaths of each group in the next time interval based on the information gathered until the end of current time interval is suggested.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.941-949
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• 2014

Unit level logistic regression model with mixed effects has been used for estimating small area proportions, which treats the spatial effects as random effects and assumes linearity between the logistic link and the covariates. However, when the functional form of the relationship between the logistic link and the covariates is not linear, it may lead to biased estimators of the small area proportions. In this paper, we relax the linearity assumption and propose two types of kernel-based logistic regression models for estimating small area proportions. We also demonstrate the efficiency of our propose models using simulated data and real data.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1309-1317
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• 2012

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.