• Knowledge Management Research
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• v.11 no.2
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• pp.95-109
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• 2010

The objectives of this study are to identify the survival function (hazard function) of small and medium enterprises by using technology rating data for the companies guaranteed by Korea Technology Finance Corporation (KOTEC), and to figure out the factors that affects their survival. To serve the purposes, this study uses Kaplan-Meier Analysis as a non-parametric method and Cox proportional hazards model as a semi-parametric one. The 17,396 guaranteed companies that assessed from July 1st in 2005 to December 31st in 2009 are selected as samples (16,504 censored data and 829 accident data). The survival time is computed with random censoring (Type III) from July in 2005 as a starting point. The results of the analysis show that Kaplan-Meier Analysis and Cox proportional hazards model are able to readily estimate survival and hazard function and to perform comparative study among group variables such as industry and technology rating level. In particular, Cox proportional hazards model is recognized that it is useful to understand which technology rating items are meaningful to company's survival and how much they affect it. It is considered that these results will provide valuable knowledge for practitioners to find and manage the significant items for survival of the guaranteed companies through future technology rating.

• Journal of applied mathematics & informatics
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• v.31 no.5_6
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• pp.711-732
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• 2013

In this paper, we are interested in finding explicit numerical formulas to evaluate defaultable bonds prices of firms. For this purpose, we use a default intensity whose values depend on the credit rating of these firms. Each credit rating corresponds to a state of the default intensity. Then, this regime switches as soon as one of the credit rating of a firm also changes. Moreover, this regime switching default intensity model allows us to capture well some market features or economics behaviors. Thus, we obtain two explicit different formulas to evaluate the conditional Laplace transform of a regime switching Cox Ingersoll Ross model. One using the property of semi-affine of the model and the other one using analytic approximation. We conclude by giving some numerical illustrations of these formulas and real data estimation results.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.783-794
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• 2019

The current ranking system of the Korean Baduk Association is based on the Elo rating system, which is widely used in the field of chess. Despite the 6.5 point dum (penalty) as compensation for playing as White, many Baduk players still prefer to playing as Black due to Black's higher winning percentage. In this paper, we present the ranking of Baduk players based on the Bradley-Terry model and address the advantage of playing as Black. We compare the ranking from our model with rankings from the Korean Baduk Association.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.190-198
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• 1993

A multiple criteria model for the facility layout problem considers both of the quantitative, the cost of the work flow, and qualitative, the closeness rating score, aspect. Rosenblatt, Fortenberry & Cox and Urban have developed multiple criteria models that consider both of the quantitative and qualitative aspect. Fortenberry & Cox's multiplicity model penalizes facilities with undesirable closeness rating and high work flows more than those undesirable closeness rating and low work flow between them to contribute to the objective function regardless of the closeness rating between these facilities. In this paper, it is intended to develops a improved multiple-criteria facility layout model considering the function for maintaning the distance between facilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.317-330
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• 2014

Bipolar disorder is a psychopathy characterized by manic and major depressive episodes. It is important to determine the degree of depression when treating patients with bipolar disorder because 810% of bipolar patients commit suicide during the periods in which they experience major depressive episodes. The Hamilton depression rating scale is most commonly used to estimate the degree of depression in a patient. This paper proposes using the Hamilton depression rating scale to estimate the effectiveness of patient treatment based on the linear mixed effects model and the transition model. Study subjects were recruited from the Seoul National University Bundang Hospital who scored 8 points or above in the Hamilton depression rating scale on their first medical examination. The linear mixed effects model and the transition model were fitted using the Hamilton depression rating scales measured at the baseline, six month, and twelve month follow-ups. Then, Hamilton depression rating scale at the twenty-four month follow-up was predicted using these models. The prediction models were then evaluated by comparing the observed and predicted Hamilton depression rating scales on the twenty-four month follow-up.

• Korean Journal of Environmental Agriculture
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• v.19 no.5
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• pp.419-425
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• 2000

Rating curve method and SWMM (Storm Water Management Model) were applied to estimate pollutant loading from Hwa-Ong watershed in Kyunggi-Do. Rating curves were derived from sampling sites and applied to the whole watershed. SWMM version 4.4 was calibrated by field data of sampling sites and applied to the whole watershed. The pollutant loading estimated by rating curve was slightly higher than the one by SWMM, but the difference was not significant considering diffuse pollution characteristics of wide variation. Land use effect of the subcatchments could not be incorporated logically in rating curve method and difficulty in extrapolation was experienced, therefore, the estimate by rating curve method was thought to be less confident. SWMM was satisfactory in estimation of pollution loading, and its great flexibility worked well to describe complex nonurban land uses. Neither of them could exactly describe complex natural phenomena, but SWMM was preferred in this study due to its flexibility and logical hydrologic processes including land use effects. Use of reasonable watershed model rather than rating curve method for watershed pollutant loading estimate can be more practical and is recommended.

• KDI Journal of Economic Policy
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• v.33 no.2
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• pp.81-110
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• 2011

Credit rating agencies(CRAs) are accused of failing to provide accurate and fair credit ratings and hence being responsible for the crisis. This paper tries to add on to the literature on credit rating reform through examining the CRAs in a model where rating quality is unobservable. We show that unobservability of rating effort results in the sub-optimal level of quality. Then the paper extends the model to incorporate ancillary services, competition and reputation. We show that ancillary services worsen the conflict of interests of the CRAs and that competition and reputation may not be strong enough to discipline the CRAs. Hence regulatory oversight and imposition of liability may be necessary in order to increase the accuracy of ratings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.721-739
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• 2023

Rating prediction is an important issue in recommender systems, and its accuracy affects the experience of the user and the revenue of the company. Traditional recommender systems use Factorization Machinesfor rating predictions and each feature is selected with the same weight. Thus, there are problems with inaccurate ratings and limited data representation. This study proposes a deep recommendation model based on self-attention Factorization (SAFMR) to solve these problems. This model uses Convolutional Neural Networks to extract features from user and item reviews. The obtained features are fed into self-attention mechanism Factorization Machines, where the self-attention network automatically learns the dependencies of the features and distinguishes the weights of the different features, thereby reducing the prediction error. The model was experimentally evaluated using six classes of dataset. We compared MSE, NDCG and time for several real datasets. The experiment demonstrated that the SAFMR model achieved excellent rating prediction results and recommendation correlations, thereby verifying the effectiveness of the model.

• Korean Management Science Review
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• v.20 no.2
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• pp.165-177
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• 2003

This paper presents the prototype of corporate credit rating system using analytic hierarchy process (AHP). Prior studios have proposed various models of credit rating system, but most studies considered only financial information. Financial information, however, is only a small part of corporate information. In this study, the proposed credit rating system integrates both financial and non-financial information. Fifteen corporations are tested for the usefulness of the proposed system.