• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.775-791
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• 2017

We frequently encounter binary data in real life. Logistic, Probit, Cauchit, Complementary log-log models are often used for binary data analysis. In order to analyze binary data, Liu (2004) proposed a Robit model, in which the inverse of cdf of the Student's t distribution is used as a link function. Kim et al. (2008) also proposed a generalized t-link model to make the binary regression model more flexible. The more flexible skewed distributions allow more flexible link functions in generalized linear models. In the sense, we propose a binary data regression model using skewed generalized t distributions introduced in Theodossiou (1998). We implement R code of the proposed models using the glm function included in R base and R sgt package. We also analyze Pima Indian data using the proposed model in R.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1305-1315
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• 2015

Consider the risk score which is a function of a linear score for the classification models. The AUC optimization method can be applied to estimate the coefficients of linear score. These estimates obtained by this AUC approach method are shown to be better than the maximum likelihood estimators using logistic models under the general situation which does not fit the logistic assumptions. In this work, the VUS and HUM approach methods are suggested by extending AUC approach method for more realistic discrimination and prediction worlds. Some simulation results are obtained with both various distributions of thresholds and three kinds of link functions such as logit, complementary log-log and modified logit functions. It is found that coefficient prediction results by using the VUS and HUM approach methods for multiple categorical classification are equivalent to or better than those by using logistic models with some link functions.

• Genomics & Informatics
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• v.18 no.1
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• pp.3.1-3.8
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• 2020

Patient-derived xenograft (PDX) mouse models are frequently used to test the drug efficacy in diverse types of cancer. They are known to recapitulate the patient characteristics faithfully, but a systematic survey with a large number of cases is yet missing in lung cancer. Here we report the comparison of genomic characters between mouse and patient tumor tissues in lung cancer based on exome sequencing data. We established PDX mouse models for 132 lung cancer patients and performed whole exome sequencing for trio samples of tumor-normal-xenograft tissues. Then we computed the somatic mutations and copy number variations, which were used to compare the PDX and patient tumor tissues. Genomic and histological conclusions for validity of PDX models agreed in most cases, but we observed eight (~7%) discordant cases. We further examined the changes in mutations and copy number alterations in PDX model production and passage processes, which highlighted the clonal evolution in PDX mouse models. Our study shows that the genomic characterization plays complementary roles to the histological examination in cancer studies utilizing PDX mouse models.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.199-209
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• 2007

Traditionally, aerial images have been used as main sources for compiling topographic maps. In recent years, lidar data has been exploited as another type of mapping data. Regarding their performances, aerial imagery has the ability to delineate object boundaries but omits much of these boundaries during feature extraction. Lidar provides direct information about heights of object surfaces but have limitations with respect to boundary localization. Considering the characteristics of the sensors, this paper proposes an approach to extracting buildings from lidar and aerial imagery, which is based on the complementary characteristics of optical and range sensors. For detecting building regions, relationships among elevation contours are represented into directional graphs and searched for the contours corresponding to external boundaries of buildings. For generating building models, a wing model is proposed to assemble roof surface patches into a complete building model. Then, building models are projected and checked with features in aerial images. Experimental results show that the proposed approach provides an efficient and accurate way to extract building models.

• Korean Management Science Review
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• v.12 no.2
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• pp.147-164
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• 1995

There have been two dominant paradigms in understanding and modeling an expert's decision-making behavior: output analysis and process-tracing. While the two paradigms are complementary, they have not been used yet in a combined manner. This study extends the previous research work in the two paradigms to inductive modeling research by 1) analyzing individual experts' decision strategies, 2) comparing performance of four popular inductive modeling methods, and 3) matching their performance against the type of decision strategy employed by experts.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.90-104
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• 2020

It is difficult for non-experts to build machine learning (ML) models at the level that satisfies their needs. Deep learning models are even more challenging because it is unclear how to improve the model, and a trial-and-error approach is not feasible since training these models are time-consuming. To assist these novice users, we examined how interactive and explainable feedback while training a deep learning network can contribute to model performance and users' satisfaction, focusing on the data preparation process. We conducted a user study with 31 participants without expertise, where they were asked to improve the accuracy of a deep learning model, varying feedback conditions. While no significant performance gain was observed, we identified potential barriers during the process and found that interactive and explainable feedback provide complementary benefits for improving users' understanding of ML. We conclude with implications for designing an interface for building ML models for novice users.

• Korea Journal of Hospital Management
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• v.25 no.1
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• pp.32-45
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• 2020

Purpose: The purpose of this research is to develop two medical tourism system models which explain medical tourism phenomenon with a systemic approach. Methodology/Approach: This research was conducted using a qualitative data analysis which mainly refer previous references in relation to medical tourism in the areas of tourism and medicine. Leiper's tourism system model was utilized as a conceptual framework. In-depth interviews with experts in the area were attempted in order to pretest the models. Findings: This research suggests a medical tourism system framework and a medical service provision framework. The first model presents medical tourism components and their relationships within a framework presented in a diagram. The second model shows the relationships among medical services required by medical tourists, the service providers, and service human resources along with movements of medical tourists. Practical Implications: The first model presents a spatial composition of medical tourism components and their relationships, whereas the second model shows the linkage among medical services, the service providers, and relevant service human resources along with time sequential steps of medical tourists. These two models are complementary and may be used as useful tools to observe medical tourism phenomenon with a systemic and holistic approach. These two models may enable stake holders avoid unnecessary confusions and conflicts that result in duplication of government policies and a waste of budget and human resources.

• Journal of Korea Trade
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• v.25 no.2
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• pp.1-21
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• 2021

Purpose - The purpose of this research is to develop two medical tourism system models which explain medical tourism phenomenon with a systemic approach. Design/methodology - This research was conducted by using a qualitative data analysis which mainly refers to previous references of medical tourism in the areas of tourism and medicine. Leiper's tourism system model was utilized as a conceptual framework. In-depth interviews with experts in the field were conducted in order to pretest the models. Findings - This research suggests a medical tourism system framework and a medical service provision framework. The first model presents medical tourism components and their relationships within a framework presented in a diagram. The second model shows the relationships among medical services required by medical tourists, the service providers, and service human resources along with movements of medical tourists. Originality/value - The first model presents a spatial composition of medical tourism components and their relationships, whereas the second model shows the linkage among medical services, the service providers, and relevant service human resources along with time sequential steps of medical tourists. These two models are complementary and may be used as useful tools to observe medical tourism phenomenon with a systemic and holistic approach. These two models may enable stake holders avoid unnecessary confusions and conflicts that result in duplication of government policies and a waste of budget and human resources.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.109-118
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• 2023

Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.

• Journal of Korean Society for Quality Management
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• v.32 no.3
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• pp.81-93
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• 2004

There is no doubt that BSC(Balanced Scorecard), ABC(Activity-Based Costing System), EVA(Economic Value Added System) draw sensation in the world as the tools of Performance Evaluation System and Quality Control. They are useful tools that can help succeed in the dynamic and competitive business environment. These three tools are discussed respectably. However, it also brings doubt whether it is possible to integrate the three tools made in the similar time and which way is appropriate among the three tools according to the type and the circumstance of business. In fact, these tools are not the opposing relations but the complementary relations. Consequently, this paper explains the relations of the three methods and suggests the process of the integrated models. Besides, it provides an idea about when Performance Evaluation System and Quality Control are needed for companies under different aspects considering the circumstance used the respective methods individually.