• Health Policy and Management
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• v.30 no.3
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• pp.399-408
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• 2020

Background: The purpose of this study was to examine the impact of the regional characteristics on the accessibility of emergency care and the impact of emergency medical accessibility on the patients' prognosis and the emergency medical expenditure. Methods: This study used the 13th beta version 1.6 annual data of Korea Health Panel and the statistics from the Korean Statistical Information Service. The sample included 8,119 patients who visited the emergency centers between year 2013 and 2017. The arrival time, which indicated medical access, was used as dependent variable for multi-level analysis. For ordinal logistic regression and multiple regression, the arrival time was used as independent variable while patients' prognosis and emergency medical expenditure were used as dependent variables. Results: The results for the multi-level analysis in both the individual and regional variables showed that as the number of emergency medical institutions per 100 km² area increased, the time required to reach emergency centers significantly decreased. Ordinal logistic regression and multiple regression results showed that as the arrival time increased, the patients' prognosis significantly worsened and the emergency medical expenses significantly increased. Conclusion: In conclusion, the access to emergency care was affected by regional characteristics and affected patient outcomes and emergency medical expenditure.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.85-94
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• 2013

As the financial market becomes larger, the loss increases due to the failure of the credit risk managements from the poor management of the customer information or poor decision-making. Thus, the credit risk management also becomes more important and it is essential to develop a credit scoring model, which is a fundamental tool used to minimize the credit risk. Credit scoring models have been studied and developed only for binary target variables. In this paper, we consider other types of target variables such as ordinal multinomial data or longitudinal binary data and suggest credit scoring models. We then apply our developed models to real data and random data, and investigate their performance through Kolmogorov-Smirnov statistic.

• Journal of Digestive Cancer Research
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• v.10 no.2
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• pp.99-106
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• 2022

Clinical research ultimately aimed to promptly diagnose and prevent diseases through precise biomarker development. Finding the optimal cut-off point of a regularly measured biomarker can help its interpretation and ultimately help in disease investigation and diagnosis, more specifically in determining the presence of diseases. Therefore, this study aimed to use the characteristics of outcome variables in clinical research to explain how to determine the optimal cutoff point. The outcome variables can be divided into dichotomous, ordinal, and survival types. The optimal cut-off point can be determined by finding points that maximize the Youden index, extended Youden index, and log-rank statistics. This study will enable clinical researchers to accurately determine the optimal cut-off points for regularly measured biomarkers, thereby enabling prompt disease diagnosis for effective treatment.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.55-69
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• 2003

In this article, we propose the weighted LAD (least absolute deviations) estimators for multi-dimensional contingency tables and drive an estimation method to estimate the proposed estimators. To illustrate the robustness of the estimators, simulation results are presented for several models Including log-linear models and models for ordinal variables in multidimensional contingency tables. Examples were also introduced.

• Journal of Veterinary Clinics
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• v.26 no.5
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• pp.401-407
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• 2009

In the previous article in this series we introduced the basic concepts for statistical analysis. The present review introduces hypothesis testing for continuous and categorical data for readers of the veterinary science literature. For the analysis of continuous data, we explained t-test to compare a single mean with a hypothesized value and the difference between two means from two independent samples or between two means arising from paired samples. When the data are categorical variables, the $x^2$ test for association and homogeneity, Fisher's exact test and Yates' continuity correction for small samples, and test for trend, in which at least one of the variables is ordinal is described, together with the worked examples. McNemar test for correlated proportions is also discussed. The topics covered may provide a basic understanding of different approaches for analyzing clinical data.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.313-322
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• 2005

In this paper, we discuss suppression for logistic regression model. Suppression for linear regression model was defined as the relationship among sums of squared for regression as well as correlation coefficients of. variables. Since it is not common to obtain simple correlation coefficient for binary response variable of logistic model, we consider cumulative logistic models with multinomial and ordinal response variables rather than usual logistic model. As number of category of a response variable for the cumulative logistic model gets collapsed into binary, it is found that suppressions for these logistic models are changed. These suppression results for cumulative logistic models are discussed and compared with those of linear model.

• Journal of the Ergonomics Society of Korea
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• v.23 no.3
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• pp.101-109
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• 2004

This paper reports the results on age and gender of Korean elderly people for the level of difficulty in performing household tasks (meal preparation, grocery shopping, house cleaning, laundry), personal tasks (dressing, bathing, grooming), transfer tasks (getting in and out of chairs, getting in and out of bath-tub, using stairs) and management tasks (using telephone, accessing mail, operating door locks). A questionnaire based on the Activities of Daily Living (ADL) scale was constructed and administered to 40 Korean elderly subjects aged from 65 to 84(mean age: 74.5, SD: 5.8) in Busan. Additionally, a logistic regression was performed with age (continuous variable) and gender as predictor variables, and reponses to individual questions as the categorical ordinal response variables. To determine appropriate age separation at which difficulty levels in performing activities of daily living change, a discriminant analysis was performed on the responses. All predictor variables were used in the analysis. Accommodating age related changes in functional abilities, and increasing functional independence of elderly people will entail significant design modifications to products, systems and environments for daily use and living.

• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.63-71
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• 2016

This paper is about applying efficient data mining method which improves the score calculation and proper building performance of credit ranking score system. The main idea of this data mining technique is accomplishing such objectives by applying Correlation based Feature Selection which could also be used to verify the properness of existing rank scores quickly. This study selected 2047 manufacturing companies on KOSPI market during the period of 2009 to 2013, which have their own credit rank scores given by NICE information service agency. Regarding the relevant financial variables, total 80 variables were collected from KIS-Value and DART (Data Analysis, Retrieval and Transfer System). If correlation based feature selection could select more important variables, then required information and cost would be reduced significantly. Through analysis, this study show that the proposed correlation based feature selection method improves selection and classification process of credit rank system so that the accuracy and credibility would be increased while the cost for building system would be decreased.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1143-1152
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• 2021

The study aims to assess the impact of factors on the access to financial services by Vietnamese farmers. The number of respondents in this study is 402 household heads participating in six diverse agricultural value chains in Vietnam. The explanatory variables of the Multinomial Logit model estimates variables at the individual characteristics while the Mixed Logit model can combine the two types of variables together to estimate the effects simultaneously. On the other hand, the Ordinal Logit model is used to evaluate the determinants of the increase in the quantity of financial services used by individuals. The estimation results show that male-headed households have more access to financial services than females. Younger farmers are more likely to use formal financial services than the elderly. Financial literacy, land ownership, and shocks in agricultural production all have a positive impact on the probability of dealing with banks. In addition, the degree of linkage and credibility of the value chain have a significant positive impact on the accessibility of financial services to farmers. The findings of this study suggest that limiting gender inequality, focusing on youth marketing and developing agricultural value chains will have a positive impact on farmers' access to financial services.

• Journal of Labour Economics
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• v.40 no.2
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• pp.1-29
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• 2017

We examine the extent to which students' performance relative to peers affects their career choice. Specifically, we analyze the relationship between a student's mathematics ranking in his/her school and the likelihood of choosing Mathematics and Science track in high school. Using a panel dataset of students in Seoul, we measure a student's performance using two variables: absolute performance and relative performance. The former measures a student's performance relative to the entire sample, while the latter measures performance relative to the student's peers in the same school. After controlling for test scores and other characteristics, we find that the students with a poor relative ranking are 11 percentage points less likely to choose the Mathematics and Science track. Relative performance affects girls more greatly than boys. Although relative performance affects a student's self-efficacy and class participation, our accounting exercise suggests that this channel accounts for only 12 percent of the impact, implying that students may respond to the relative ranking mostly due to other factors, such as strategic consideration to perform well in college applications.