• Korean Journal of Agricultural Science
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• v.44 no.4
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• pp.620-635
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• 2017

This study was intended to identify the main factors responsible for the decline in purchase of imported agricultural and fish products after Japan's nuclear power plant accident in 2011 and to compare the effects on imported agricultural produce and imported fish products. Logit model and multiple regression model analyses were performed using consumers' survey data. Psychological and qualitative factors reflecting consumers' food safety awareness and purchasing preferences, which were extracted by Factor analysis, were included as the models' explanatory variables, along with socio-demographic and economic factors. The Logit estimation showed aged, married, and low-income households had significantly higher probability of reducing their purchases of imported agricultural and fish products. However, the multiple regression results pointed out that the actual rate of decrease of imported agricultural and fish products purchases were more significantly affected by non-socio demographic factors such as past experience of purchasing imported agricultural and fish products, future intention to purchasing Japanese agricultural and fish products, and the ratio of imported to domestic agricultural and fish products before the nuclear accident, as well as consumers' feeling of food insecurity and their purchasing preferences. Moreover, the results showed that Korean consumers have reacted more sensitively to the decline in imported fish products than imported agricultural produce after the nuclear accident based on the marginal effects of various socio-demographic and economic factors.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.373-380
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• 2013

Recently, AR(Augmented Reality) system as an information technology for the increased access of information has a potential possibility of next generation's system for tourism guide. In this regard, the objective of this study is to explore the technology acceptance factors of AR system on tourism destination. To achieve the objective of this study, logit regression model was used to analyze the influential level of the factors. This study was analyzed with the final 224 respondents and the results showed that if there will be assured with high trust and easy access via mobility device as smartphone, the AR system has the possibility of high acceptance level. The result of this study will be expected to be utilized as fundamental data from the viewpoint of the service providers and system developers that want to launch the appropriate service to users' needs of AR system.

• Journal of Korean Academy of Nursing
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• v.43 no.2
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• pp.154-164
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• 2013

Purpose: The purpose of this article is twofold: 1) introducing logistic regression (LR), a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, and 2) examining use and reporting of LR in the nursing literature. Methods: Text books on LR and research articles employing LR as main statistical analysis were reviewed. Twenty-three articles published between 2010 and 2011 in the Journal of Korean Academy of Nursing were analyzed for proper use and reporting of LR models. Results: Logistic regression from basic concepts such as odds, odds ratio, logit transformation and logistic curve, assumption, fitting, reporting and interpreting to cautions were presented. Substantial shortcomings were found in both use of LR and reporting of results. For many studies, sample size was not sufficiently large to call into question the accuracy of the regression model. Additionally, only one study reported validation analysis. Conclusion: Nursing researchers need to pay greater attention to guidelines concerning the use and reporting of LR models.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• Journal of the Korean housing association
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• v.26 no.5
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• pp.133-141
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• 2015

As an empirical study on Daegu Downtown showing decentralization phenomenon, the purpose of this study is, based on the residential satisfaction research targeting the Downtown residents, to analyze the residential environment satisfaction and residential preference. Considering the parameters of measurement, we used the Ordered Logit Model and Logistic Regression. The results are as follows: First, the comprehensive residential environment satisfaction is relatively lower than that in 2008 and the residential preference in Downtown is similar to that of the past. Second, among the 7 factors that constitute the Downtown residential environment, the house, the landscape, and the security have a relatively large influence on the comprehensive residential environment satisfaction. Third, the residential environment factors which affect those who are hoping continuous Downtown residence are the safety, the house and the complex.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.293-300
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• 2012

When Binomial data are obtained, logit and logit mixed models are commonly used for small area estimation. Those models are known to have good statistical properties through the use of unit level information; however, data should be obtained as area level in order to use area level information such as spatial correlation or auto-correlation. In this research, we suggested a new small area estimator obtained through the combination of unit level information with area level information.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.367-382
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• 2018

Prediction models for a corporate bond rating in existing studies have been developed using various models such as linear regression, ordered logit, and random forest. Financial characteristics help build prediction models that are expected to be contained in the assigning model of the bond rating agencies. However, the ranges of bond ratings in existing studies vary from 5 to 20 and the prediction models were developed with samples in which the target companies and the observation periods are different. Thus, a simple comparison of the prediction accuracies in each study cannot determine the best prediction model. In order to conduct a fair comparison, this study has collected corporate bond ratings and financial characteristics from 2013 to 2017 and applied prediction models to them. In addition, we applied the elastic-net penalty for the linear regression, the ordered logit, and the ordered probit. Our comparison shows that data-driven variable selection using the elastic-net improves prediction accuracy in each corresponding model, and that the random forest is the most appropriate model in terms of prediction accuracy, which obtains 69.6% accuracy of the exact rating prediction on average from the 5-fold cross validation.

• 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 Korea Society of Computer and Information
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• v.20 no.3
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• pp.47-56
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• 2015

Automated detection of interesting web pages could be used in many different application domains. Determining a user's interesting web pages can be performed implicitly by observing the user's behavior. The task of distinguishing interesting web pages belongs to a classification problem, and we choose white box learning methods (fixed effect logit regression and support vector machine) to test empirically. The result indicated that (1) fixed effect logit regression, fixed effect SVMs with both polynomial and radial basis kernels showed higher performance than the linear kernel model, (2) a personalization is a critical issue for improving the performance of a model, (3) when asking a user explicit grading of web pages, the scale could be as simple as yes/no answer, (4) every second the duration in a web page increases, the ratio of the probability to be interesting increased 1.004 times, but the number of scrollbar clicks (p=0.56) and the number of mouse clicks (p=0.36) did not have statistically significant relations with the interest.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.753-763
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• 2014

After retirement, retirees are exposed to many changes. But, one of the most influential factor on retirement is Social network. Social network is making various relationships with many people. This consists of functional feature and structure feature. This study to systematically investigate social network changes of pre- and post-retirement by using two features. We utilized 2008~2012 'Longitudinal study of ageing' and selected 1,569 retirees above 45 years old as a final subject. This study used STATA 12.0 program for analysing frequency and descriptive statistic. At first, we analyzed personal characteristics and affecting factor on social network of retirees through Panel logit model and fixed effects regression model. Second, we applied multiple panel logit model and fixed effects model to learn factor affecting employment and social network changes. We found that a number of social activities affects social network in the structure feature and support from sons and daughters also influences social network in the functional feature after retirement.