• The Korean Journal of Health Service Management
• /
• v.9 no.2
• /
• pp.81-88
• /
• 2015

The purpose of this study was to compare the risk-adjusted in-hospital mortality for craniotomies between logistic regression and multilevel analysis. By using patient sample data from the Health Insurance Review & Assessment Service, in-patients with a craniotomy were selected as the survey target. The sample data were collected from a total number of 2,335 patients from 90 hospitals. The sample data were analyzed with SAS 9.3. From the results of the existing logistic regression analysis and multilevel analysis, the values from the multilevel analysis represented a better model than that of logistic regression. The intra-class correlation (ICC) was 18.0%. It was found that risk-adjusted in-hospital mortality for craniotomies may vary in every hospital. The agreement by kappa coefficient between the two methods was good for the risk-adjusted in-hospital mortality for craniotomies, but the factors influencing the outcome for that were different.

• Communications for Statistical Applications and Methods
• /
• v.25 no.2
• /
• pp.199-215
• /
• 2018

Interest in $PM_{10}$ concentrations have increased greatly in Korea due to recent increases in air pollution levels. Therefore, we consider a forecasting model for next day $PM_{10}$ concentration based on the principal elements of air pollution, weather information and Beijing $PM_{2.5}$. If we can forecast the next day $PM_{10}$ concentration level accurately, we believe that this forecasting can be useful for policy makers and public. This paper is intended to help forecast a daily mean $PM_{10}$, a daily max $PM_{10}$ and four stages of $PM_{10}$ provided by the Ministry of Environment using various data mining techniques. We use seven models to forecast the daily $PM_{10}$, which include five regression models (linear regression, Randomforest, gradient boosting, support vector machine, neural network), and two time series models (ARIMA, ARFIMA). As a result, the linear regression model performs the best in the $PM_{10}$ concentration forecast and the linear regression and Randomforest model performs the best in the $PM_{10}$ class forecast. The results also indicate that the $PM_{10}$ in Seoul is influenced by Beijing $PM_{2.5}$ and air pollution from power stations in the west coast.

• ALGAE
• /
• v.23 no.4
• /
• pp.289-294
• /
• 2008

To estimate seaweed biomass or standing crop, a nondestructive sampling can be beneficial because of not much destroying living plants and saving time in field works. We suggest a methodological procedure to estimate seaweed biomass per unit area in marine benthic habitats by using species-specific regression equations. Percent cover data are required from the field samplings for most species to convert them to weight data. However, for tall macroalgae such as kelps we need density data and their size (e.g., size class for subtidal kelps) of individuals. We propose that the field sampling should be done with 5 replicates of 50 cm x 50 cm quadrat at three zones of intertidals (upper, middle, lower) and three depth points (1, 5, 10 m) in subtidals. To obtain a reliable regression equation for a species, a substantial number of replicate is necessary from destructive samplings. The regression equation of a species can be further specified by different locality and different season, especially for the species with variable morphology temporally and spatially. Example estimation carried out in Onpyung, Jeju Island, Korea is provided to compare estimated values with real weight data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.8
• /
• pp.3086-3101
• /
• 2021

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped 𝑙_1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the 𝑙₁ norm and k-support norm, the proposed grouped 𝑙_1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex 𝑙₀ sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, 𝑙_1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.

• 한국노년학
• /
• v.40 no.1
• /
• pp.33-49
• /
• 2020

This study was conducted to examine the latent classes in social exclusion and to analyse empirically the effects on the economic instability of old age by this type. And it also sought to look at whether the influence of old age anxiety varies with the subjective class consciousness of the elderly. Using the 14th data from the Korea General Social Survey (KGSS) in 2016, 1,041 adult males and females aged 18 years old were analyzed at the time of the survey. T-test, potential layer analysis (LCA), and multinomantic analysis of potential groups were conducted using the STATA14 and MPLUS 7 statistical programs. Finally, multi-regression analysis was performed to identify the moderate effect and effects among variables. According to the research, the types of social exclusion were three groups, followed by social exclusion group (49.3%), Multi-dimensional exclusion group (30.9%), and active social participation group (19.7%). The social exclusion group has the lowest possibility of economic, employment, and health exclusion, but the exclusion of formal and informal social activities seem to prominent, and the multi-dimensional exclusion group is more than 50% likely to experience exclusion in all areas. Active social participation are characterized by very active participation in informal social activities. By conducting multinominal logistic regression, it was observed that the social exclusion group included more young people than other groups, and that the multi-dimensional exclusion group included many elderly women without spouses. Finally, multiple regression analysis showed that social exclusion type interacts with subjective class consciousness and affects economic anxiety of old age.

• Journal of the Korean Statistical Society
• /
• v.24 no.1
• /
• pp.127-139
• /
• 1995

This paper is concerned with the asymptotic properties of the least absolute deviation estimators for nonlinear regression models. The simple and practical sufficient conditions for the strong consistency and the asymptotic normality of the least absolute deviation estimators are given. It is confirmed that the extension of these properties to wide class of regression functions can be established by imposing some condition on the input values. A confidence region based on the least absolute deviation estimators is proposed and some desirable asymptotic properties including the asymptotic relative efficiency also discussed for various error distributions. Some examples are given to illustrate the application of main results.

• Communications for Statistical Applications and Methods
• /
• v.19 no.5
• /
• pp.663-671
• /
• 2012

In the present study, a combined ratio cum regression estimator is proposed to estimate the population mean of the study variable in the presence of a non-response using an auxiliary variable under double sampling. The expressions of bias and mean squared error(MSE) based on the proposed estimator is derived under double (or two stage) sampling to the first degree of approximation. Some estimators are also derived from the proposed class by allocating the suitable values of constants used. A comparison of the proposed estimator with the usual unbiased estimator and other derived estimators is carried out. An empirical study is carried out to demonstrate the performance of the suggested estimator and of others; it is endow that the empirical results backing the theoretical study.

• Journal of the Korean Society of Industry Convergence
• /
• v.22 no.6
• /
• pp.749-756
• /
• 2019

Big data is structured and unstructured data that is so difficult to collect, store, and so on due to the huge amount of data. Many institutions, including universities, are building student convergence systems to foster talents for data science and AI convergence, but there is an absolute lack of research on what kind of education is needed and what kind of education is required for students. Therefore, in this paper, after conducting the correlation analysis based on the questionnaire on basic surveys and courses to improve the curriculum by grasping the satisfaction and demands of the participants in the "2019 Big Data Youth Talent Training Course" held at K University, Regression analysis was performed. As a result of the study, the higher the satisfaction level, the satisfaction with class or job connection, and the self-development, the more positive the evaluation of program efficiency.

• Journal of Digital Convergence
• /
• v.16 no.4
• /
• pp.289-297
• /
• 2018

The purpose of this study was to examine the correlations of learning attitude, class participation, and learning satisfaction, and to identify the influencing factors on learning satisfaction of nursing students in fundamental nursing curriculum. Data were collected from 173 nursing students from September 1 to 8, 2017. Data were analyzed using Pearson's correlation coefficients and stepwise multiple regression. Learning attitude, class participation, and learning satisfaction according to the general characteristics commonly showed significant differences in satisfaction with major, satisfaction with school, friendship, last semester grade, and interest in fundamental nursing. Learning satisfaction showed significant positive correlations with learning attitude and class participation. Satisfaction with major, interest in fundamental nursing, and class participation, which accounted for 36% of the variance, were significant predictors influencing learning satisfaction in nursing students. It is necessary to increase majors' satisfaction, fundamental nursing interest, and class participation in order to improve learning satisfaction of nursing students.

• Journal of Preventive Medicine and Public Health
• /
• v.39 no.6
• /
• pp.469-476
• /
• 2006

Objectives : To investigate the effect of parents' social class on infant and child mortality rates among the birth cohort, for the period of transition to and from the Koran economic crisis 1995-2004. Methods : All births reported to between 1995 and 2004 (n=5,711,337) were analyzed using a Cox regression model, to study the role of the social determinants of parents in infant and child mortality. The results were adjusted for the parents' age, education and occupation, together with mother's obstetrical history. Results. The crude death rate among those under 10 was 3.71 per 1000 births (21,217 deaths among 5,711,337 births) between 1995 and 2004. The birth cohorts from lower educated parents less than elementary school showed higher mortality rates compared with those from higher educated parents over university level (HR:3.0 (95%CI:2.8-3.7) for father and HR:3.4 (95%CI:3.3-4.5) for mother). The mother's education level showed a stronger relationship with mortality among the birth cohort than that of the fathers. The gaps in infant mortality rates by parents' social class, and educational level became wider from 1995 to 2004. In particular, the breadth of the existing gap between higher and lower parents' social class groups has dramatically widened since the economic crisis of 1998. Discussions : This study shows that social differences exist in infant and child mortality rates. Also, the gap for the infant mortality due to social class has become wider since the economic crisis of 1998.