• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.27-41
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• 2008

A graphical method of checking the adequacy of a generalized linear model is proposed. The graph helps to assess the assumption that the link function of mean can be expressed as a linear combination of explanatory variables in the generalized linear model. For the graph the boosting technique is applied to estimate nonparametrically the relationship between the link function of the mean and the explanatory variables, though any other nonparametric regression methods can be applied. Through simulation studies with normal and binary data, the effectiveness of the graph is demonstrated. And we list some limitations and technical details of the graph.

• Journal of Korean Biological Nursing Science
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• v.21 no.2
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• pp.108-113
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• 2019

Purpose: The purpose of this study was to investigate the difference in serum ferritin and leukocyte regarding overweight and obese South Korean adults. Methods: This study was conducted on 5,281 subjects older than 19, according to data from the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V-3), 2015. Data were analyzed using descriptive statistics, t-test, ANOVA, Scheffe's test, Pearson's correlation coefficient, and stepwise multiple regression analysis (SPSS 24.0). Results: First, serum ferritin and leukocyte were higher regardubg obesity, followed by being overweight and within normal weight. Second, body mass index (BMI) was positively correlated with serum ferritin and leukocyte. Third, factors affecting serum ferritin were gender, and being obese and overweight. Explanatory power of the model was 26.2%. Factors affecting leukocyte were gender, obesity, being overweight, and weight change over the past year (weight gain). Explanatory power of the model was 10.2%. Conclusion: Obesity and being overweight were factors affecting serum ferritin and leukocyte, and obesity was more affected than being overweight in Koreans older than 19. In conclusion, serum ferritin was a marker of inflammation, rather than iron status, in overweight and obese Korean adults.

• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.411-426
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• 2023

Purpose - The purpose of this study was to explore and diagnose the characteristics and behavioural patterns of rural tourists after COVID-19 using decision tree analysis to classify and identify key segmentation groups. Design/methodology/approach - The CHAID algorithm was used as the analysis technique for the decision tree. The explanatory variables used in the analysis of each decision tree model were demographic variables and rural tourism usage behaviour and perception variables, and the target variables were the preferences of rural tourists' activities after COVID-19. From the Rural Tourism 2020 survey data, 614 samples with rural tourism experience were extracted and used in the analysis. Findings - The variables that significantly explained the preference for each type of rural tourism activity after COVID-19 were rural tourism safety perception, repeated visits to the region, rural tourism priority activity, rural tourism accommodation experience, gender, age group, marital status, occupation, and education level. Among them, rural tourism safety perception was the most important explanatory variable in each analysis model. Research implications or Originality - Overall, to promote rural tourism, it is necessary to enhance the safety image of rural tourism, strengthen loyalty programs for repeat visitors, and develop customized products that reflect the preferred trends of rural tourism.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.635-650
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• 2010

We model surrender rates with a few explanatory variables such as the difference between reference marke rates and product crediting rates, the policy age since the contract was issued, unemployment rates, economy growth rates, and seasonal effects using logit function. We investigate the policy holder surrender behaviors of US single premium deferred annuities(SPDA) and Korean interest indexed annuities under extreme financial conditions.

• Journal of the Korean Statistical Society
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• v.8 no.2
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• pp.107-109
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• 1979

Consider a linear model with n observations and k explanatory variables: (1)b $y=X\beta+u, u\simN(0,\sigma^2I_n)$. We assume that the model satisfies the ideal conditions. Consider the general linear constraints on regression coefficient vector: (2) $R\beta=r$, where R and r are known matrices of orders $q\timesk$ and q\times1$ respectively, and the rank of R is $qk+q$.

• Korean System Dynamics Review
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• v.15 no.4
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• pp.129-159
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• 2014

This Study discuss exploring normative-prescriptive factors after the themes on Organizational learning categorize two descriptive/explanatory-perspectives, prescriptive/normative dimension. The former would contain information processing model, theory of action, organizing in organization, while Senge's suggestion on Learning Organization may compose the latter. Each perspective is reconstructed and reinterpreted into the causal mapping relationship founded on system thinking and SD. Underlying on the former try to discovery validities of the latter. But this study only put forward the integral-dynamic model of organizational learning without empirical simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.129-132
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• 1996

In this paper, we suggest a framework to represent the fuzziness in knowledge base as a perspective of the object-oriented paradigm. We divide the knowledge base in two parts. One is the object-base that stores the fuzzy propositions and the explanatory databases. The other is the rule-base that manages the rules between the fuzzy propositions. As the first step, we have to develop a new fuzzy object model that gives an easy way to represent the fuzzy propositions, that is, the fuzzy knowledge in the real world.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.251-259
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• 2018

This study was conducted to investigate individuals' mental health on the basis of scores for depression and self-esteem from the Korea Welfare Panel Study (KWPS) and determine their dynamic association with employment status. This study was conducted using the eighth (2013) and ninth (2014) data from the KWPS. To identify factors affecting depression, socio-demographic factors, factors related to mental health, and contents related to employment, such as changes in the employment condition and employment status, were selected and analyzed by t-test, ANOVA, and hierarchial multiple regression to determine the explanatory power. Multiple regression revealed that in Model 1, those who were female, were older, had no spouse, were in the lower income bracket, had lower total self-esteem, and scored higher for depression in the previous period tended to show higher levels of depression. Education had no significant effect and explanatory power for all variables inputted into this model was estimated to be 30.8% (p<0.001). The explanatory power for all variables input into Model 2, which was generated by inputting employment status into Model 1, was estimated to be 30.9%, which was 0.1% higher than for Model 1 (p<0.05). These results indicated that depression was significantly correlated with gender, age, income, presence of a spouse, previous depression, self-esteem, and employment status; accordingly, investigation of the factors that can narrow the gap among variables affecting depression should be conducted and socially supported.

• Food Science and Preservation
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• v.30 no.3
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• pp.459-471
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• 2023

The Bayesian network (BN) model was applied to analyze the characteristic variables that affect compliance with safety inspections of farmed eel during the production stage, using the data from 30,063 cases of eel aquafarm safety inspection in the Integrated Food Safety Information Network (IFSIN) from 2012 to 2021. The dataset for establishing the BN model included 77 non-conforming cases. Relevant HACCP data, geographic information about the aquafarms, and environmental data were collected and mapped to the IFSIN data to derive explanatory variables for nonconformity. Aquafarm HACCP certification, detection history of harmful substances during the last 5 y, history of nonconformity during the last 5 y, and the suitability of the aquatic environment as determined by the levels of total coliform bacteria and total organic carbon were selected as the explanatory variables. The highest achievable eel aquafarm noncompliance rate by manipulating the derived explanatory variables was 24.5%, which was 94 times higher than the overall farmed eel noncompliance rate reported in IFSIN between 2017 and 2021. The established BN model was validated using the IFSIN eel aquafarm inspection results conducted between January and August 2022. The noncompliance rate in the validation set was 0.22% (15 nonconformances out of 6,785 cases). The precision of BN model prediction was 0.1579, which was 71.4 times higher than the non-compliance rate of the validation set.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.365-377
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• 2020

We discuss latent factor regression when constructing a common structure inherent among explanatory variables to solve multicollinearity and use them as regressors to construct a linear model of a response variable. Bayesian estimation with LASSO prior of a large penalty parameter to construct a significant factor loading matrix of intrinsic interests among infinite latent structures. The estimated factor loading matrix with estimated other parameters can be inversely transformed into linear parameters of each explanatory variable and used as prediction models for new observations. We apply the proposed method to Product Service Management data of HBAT and observe that the proposed method constructs the same factors of general common factor analysis for the fixed number of factors. The calculated MSE of predicted values of Bayesian latent factor regression model is also smaller than the common factor regression model.