• 무역학회지
• /
• 제47권2호
• /
• pp.69-88
• /
• 2022

This paper aims to predict Busan's regional product and employment using the logistic regression models and machine learning models. The following are the main findings of the empirical analysis. First, the OLS regression model shows that the main industries such as electricity and electronics, machine and transport, and finance and insurance affect the Busan's income positively. Second, the binomial logistic regression models show that the Busan's strategic industries such as the future transport machinery, life-care, and smart marine industries contribute on the Busan's income in large order. Third, the multinomial logistic regression models show that the Korea's main industries such as the precise machinery, transport equipment, and machinery influence the Busan's economy positively. And Korea's exports and the depreciation can affect Busan's economy more positively at the higher employment level. Fourth, the voting ensemble model show the higher predictive power than artificial neural network model and support vector machine models. Furthermore, the gradient boosting model and the random forest show the higher predictive power than the voting model in large order.

• 인삼문화
• /
• 제5권
• /
• pp.97-114
• /
• 2023

인삼에 관련된 수많은 연구에도 불구하고, 인삼을 경제, 경영학적 관점에서 다루는 논문은 부족한 편이다. 최근 인삼제품을 구매하는 원인을 경제학적으로 밝히려는 연구가 주목을 받고 있다. 본 연구는 미시경제학적 관점에서 국제관광소비자의 인삼구매 요인을 실증적으로 분석하는 것을 목적으로 한다. 이를 위하여 설문자료를 통해 대한민국을 방문하는 국제 관광소비자가 인삼 및 인삼 관련 제품을 구매하는 형태를 실증적으로 조사하였다. 그 결과를 이용하여 인삼을 구매하는 데 영향을 미치는 결정요인을 다항 로지스틱 모형(multinomial logistic model)을 활용하여 분석하였다. 분석 결과 다음과 같은 인삼 구매 결정요인을 발견하였다. 첫째, 경제적 요인은 인삼구매의 중요한 결정 요인이 가구소득보다 가처분소득(일일평균 지출비용)이 인삼 구매에 미치는 영향이 컸다. 일일평균 지출비용이 높음에도 불구하고 다른 제품을 더 선호하면 인삼을 덜 구매하는 것으로 나타났다. 둘째, 인구통계학적 요인 중 성별과 나이 또한 인삼 구매의 중요한 결정 요인이다. 나이가 많은 남성 소비자층의 인삼제품 구매 확률이 높은 것으로 밝혀졌다. 셋째, 관광 목적은 인삼 구매에 영향을 끼친다. 여가 목적의 관광소비자가 다른 목적의 관광소비자보다 인삼 구매 확률이 높았다. 마지막으로 관광지 속성도 인삼 구매의 결정 요인이다. 관광지의 치안(안전성), 모바일/인터넷 사용 편리성과 길찾기 용이성 등의 관광지 속성은 인삼 구매에 영향을 미치는 중요한 요인이었다. 특히 쇼핑의 편리함을 의미하는 모바일/인터넷 사용 편리성과 길찾기 용이성이 관광소비자의 인삼구매 확률을 높이는 것으로 나타났다. 이 연구는 국제 관광소비자의 인삼 선택을 선호(preference)에 기반한 계량경제학적 모델로 분석하여 인삼제품 구매 선택의 결정요인을 탐구하였다는 점에서 의의가 있다.

• Journal of the Korean Data and Information Science Society
• /
• 제28권3호
• /
• pp.685-695
• /
• 2017

The traditional mixture of experts (ME) modeled the gate network using a certain parametric function. However, if the assumed parametric function does not properly reflect the true nature, the prediction strength of ME would become weak. For example, the parametric ME often uses logistic or multinomial logistic models for the network model. However, this could be very misleading if the true nature of the data is quite different from those models. Although, in this case, we may develop more flexible parametric models by extending the model at hand, we will never be free from such misspecification problems. In order to alleviate such weakness of the parametric ME, we propose to use the semi-parametric mixture of experts (SME) in which the gate network is estimated in a non-parametrical way. Based on this, we compared the performance of the SME with those of ME and neural networks via several simulation experiments and real data examples.

• Communications for Statistical Applications and Methods
• /
• 제30권6호
• /
• pp.577-587
• /
• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.

• 지역사회간호학회지
• /
• 제30권4호
• /
• pp.449-459
• /
• 2019

Purpose: The aim of this study is to explore different types of self-rated health trajectories among one-person households in Korea. Methods: We used five time-point data derived from Korea Health Panel (2011~2015). A latent growth curve modeling was used to assess the overall feature of self-rated health trajectory in one-person households, and a latent class growth modeling was used to determine the number and shape of trajectories. We then applied multinomial logistic regression on each class to explore the predicting variables. Results: We found that the overall slope of self-rated health in one-person households decreases. In addition, latent class analysis demonstrated three classes: 1) High-Decreasing class (i.e., high intercept, significantly decreasing slope), 2) Moderate-Decreasing class (i.e., average intercept, significantly decreasing slope), and 3) Low-Stable class (i.e., low intercept, flat and nonsignificant slope). The multinomial logistic regression analysis showed that the predictors of each class were different. Especially, one-person households with poor health condition early were at greater risk of being Low-Stable class compared with High-Decreasing class group. Conclusion: The findings of this study demonstrate that more attentions to one-person households are needed to promote their health status. Policymakers may develop different health and welfare programs depending on different characteristics of one-person household trajectory groups in Korea.

• 한국치위생학회지
• /
• 제16권1호
• /
• pp.129-136
• /
• 2016

Objectives: The purpose of the study is to investigate the relationship between oral health status and body mass index (BMI) in adults over 65 years old. Methods: The study subjects were 4,550 adults over 65 years old from the 5th Korea National Health and Nutrition Examination Survey(KNHANES V) in 2010-2012. Mastication-related oral health status included the number of remaining teeth, and mean number of decayed, missing, and filled permanent teeth(DMFT). Body mass index(BMI, $kg/m^2$) was categorized as underweight(<18.5), normal weight (18.5-22.9), overweight(23.0-24.9), and obese(${\geq}25.0$). Multinomial logistic regression analysis was performed to examine the association of BMI categories with the number of remaining teeth and DMFT. Results: The mean number of DMFT was highest($13.0{\pm}0.7$) in the underweight group and lowest($8.8{\pm}0.3$) in the obese group. Those having less favorable masticatory ability, and fewer number of remaining teeth and no prosthesis, tended to be underweight. Those having a higher number of remaining teeth and prosthetic teeth tended to be overweight or obese. In the multinomial logistic regression analysis, compared with those having 20 or more remaining teeth, including prosthetic teeth, those having less than 20 remaining teeth and no prosthesis had 4.48 times higher odds ratio of being underweight. DMFT was positively associated with underweight, while negatively associated with overweight or obesity. Conclusions: The masticatory ability and dental caries prevention maintained the healthy body weight in adults of old age.

• 산업경영시스템학회지
• /
• 제41권2호
• /
• pp.84-94
• /
• 2018

In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies' attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

• 대한두개안면성형외과학회지
• /
• 제13권1호
• /
• pp.36-40
• /
• 2012

Purpose: Statistical analysis of facial bone fractures has been performed in various papers. However, reports on risk factors for facial bone fractures are rare. In order to prevent facial bone fractures, it is important to determine the risk factors for their occurrence. This study seeks to perform a statistical analysis on and identify the risk factors associated with facial bone fractures. Methods: A retrospective study was performed to assess facial bone fractures in patients presenting from October 2009 to January 2011 through a chart review. The data collected included age, gender, etiology, and alcohol consumption. Data was analyzed using multinomial logistic regression analysis. The significance level was set at p<0.05 and SAS ver. 9.2 was used. Results: A total of 489 patients were analyzed. The patients' age ranged from 2 to 85 years (mean age, $31.8{\pm}15.4$ years). The ratio of men to women was 5.0:1. The predominant group was age below 19 years old (30.9%). The main causes of facial bone fractures were assaults (37.8%), falls (27.2%), and sport accidents (19.5%). On multinomial logistic regression analysis, age, especially in the teen group was associated with assaults (p<0.05) resulting in facial bone fractures. Alcohol consumption was significantly associated with assaults and falls (p<0.05) leading to facial bone fractures. Conclusion: Facial bone fracture is a challenging problem, because of its high incidence and financial cost. The findings of this study indicate that more effective policies aimed at reducing alcohol intake and teenage violence are needed.

• 농촌계획
• /
• 제26권3호
• /
• pp.1-12
• /
• 2020

The purpose of this study is to analysis the influence of fishing villages experience programs choice by the tourist characteristics. As an analysis method, a statistical technique of multinomial logistic regression was used. The dependent variable have typified about 70 fishing experience programs, such as tidal-flat experience, fishery experience, and fishing experience, operated by the fishing village experience recreation villages into 9 programs. The independent variables consisted of 7 groups of people: gender, age, marital status, presence of children, experience of visiting a village in a rural and fishing village experience, preference of a village in a recreational experience, and recognition of a village in a fishing village experience. As a result of analysis, no significant differences were found that the selection group preferring 'fishing culture experience', 'leports experience', 'ecological craft experience', and 'festival and event experience' in the selection of fishing village experience program compared to the group choosing 'rural experience'. On the other hand, the group preferring 'tidal flat experience' analysis that 'married' is about 14 times higher than 'unmarried', and the group preferring 'fishing village experience' is 9.55 times higher than the group preferring 'rural village experience'. In the group preferring 'fishery experience' and 'fishing experience', the group preferring 'fishing experience recreation village' was 9.21 times and 14.34 times higher than the group preferring 'rural experience recreation village'. In the 'food experience', 'married' was 25 times higher than 'unmarried'.

• 한국융합학회논문지
• /
• 제10권2호
• /
• pp.75-81
• /
• 2019

본 연구는 통계적인 기법을 융합 활용하여 국내 갑상선암과 관련된 연구 토픽의 동향 및 변화 추세를 알아보기 위함이다. DBpia에 등록되어 있는 갑상선암 관련 논문을 대상으로 LDA(latent Dirichlet allocation) 기반의 토픽 모형을 적용한 결과, 4개의 연구 토픽을 도출하였으며 각 토픽은 "Surgery", "Disease aggressiveness", "Survival analysis", "Well-being of patients"에 관한 내용으로 확인되었다. 다범주 로짓모형을 이용하여 연구 토픽의 시대적 추이를 확인한 결과, 2000년 이전에는 "Surgery", 2000년대에는 "Disease aggressiveness"와 "Survival analysis", 2010년 이후에는 "Survival analysis"와 특히 "Well-being of patients"에 관한 연구가 많이 이루어졌음을 확인하였다. 이는 향후 갑상선암 연구의 방향 모색에 필요한 기초자료로 활용될 수 있을 것이며, 최근 환자의 복지로 크게 전환된 연구 토픽의 변화가 다른 질병에서도 관찰되는지 추후 검토할 필요가 있다.