• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.579-588
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• 2020

Obligor defaults are cross-sectionally correlated as obligors share common economic conditions; in addition obligors are longitudinally correlated so that an economic shock like the IMF crisis in 1998 lasts for a period of time. A longitudinal correlation should be used to construct statistical scenarios of stress test with which we replace a type of artificial scenario that the banks have used. We propose a Bayesian model to accommodate such correlation structures. Using 402 obligors to a domestic bank in Korea, our model with a dynamic correlation is compared to a Bayesian model with a stationary longitudinal correlation and the classical logistic regression model. Our model generates statistical financial statement under a stress situation on individual obligor basis so that the genearted financial statement produces a similar distribution of credit grades to when the IMF crisis occurred and complies with Basel IV (Basel Committee on Banking Supervision, 2017) requirement that the credit grades under a stress situation are not sensitive to the business cycle.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.319-331
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• 2022

In this paper, we develop a new statistical model to forecast the PM_2.5 level in Seoul, South Korea. The proposed model is based on the extreme quantile regression model with lasso penalty. Various meteorological variables and air pollution variables are considered as predictors in the regression model, and the lasso quantile regression performs variable selection and solves the multicollinearity problem. The final prediction model is obtained by combining various extreme lasso quantile regression estimators and we construct a binary classifier based on the model. Prediction performance is evaluated through the statistical measures of the performance of a binary classification test. We observe that the proposed method works better compared to the other classification methods, and predicts 'very bad' cases of the PM_2.5 level well.

• Journal of the Semiconductor & Display Technology
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• v.12 no.1
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• pp.1-7
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• 2013

Grey-level statistical models have been widely used in many applications for object location and identification. However, conventional models yield some problems in model refinement when training images are not properly aligned, and have difficulties for real-time recognition of arbitrarily rotated models. This paper presents improved grey-level statistical models that align training images using image or feature matching to overcome problems in model refinement of conventional models, and that enable real-time recognition of arbitrarily rotated objects using efficient hierarchical search methods. Edges or features extracted from a mean training image are used for accurate alignment of models in the search image. On the aligned position and orientation, fitness measure based on grey-level statistical models is computed for object recognition. It is demonstrated in various experiments in PCB inspection that proposed methods are superior to conventional methods in recognition accuracy and speed.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.18 no.2
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• pp.129-142
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• 2014

We propose a variational segmentation model based on statistical information of intensities in an image. The model consists of both a local region-based energy and a global region-based energy in order to handle misclassification which happens in a typical statistical variational model with an assumption that an image is a mixture of two Gaussian distributions. We find local ambiguous regions where misclassification might happen due to a small difference between two Gaussian distributions. Based on statistical information restricted to the local ambiguous regions, we design a local region-based energy in order to reduce the misclassification. We suggest an algorithm to avoid the difficulty of the Euler-Lagrange equations of the proposed variational model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.981-996
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• 2008

Statistical matching techniques whose aim is to achieve a complete data file from different sources. Since the statistical matching method proposed by Rubin (1986) assumes the multivariate normality for data, using this method to data which violates the assumption would involve some problems. This research proposed the statistical matching method using robust regression as an alternative to the linear regression. Furthermore, we carried out a simulation study to compare the performance of the robust regression model and the linear regression model for the statistical matching.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.1-18
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• 1995

The generalized logit model of nominal type with random regressors is studied for bootstrapping. In particular, asymptotic normality and consistency of bootstrap model estimators are derived. It is shown that the bootstrap approximation to the distribution of the maximum likelihood estimators is valid for alsomt all sample sequences.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.87-96
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• 2000

Nonlinear time series prediction is derived and compared between statistic of modeling and neural network method. In particular mean squared errors of predication are obtained in generalized random coefficient model and generalized autoregressive conditional heteroscedastic model and compared with them by neural network forecasting.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.115-134
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• 1994

Polynomial errors-in-variables model with one predictor variable and one response variable is defined and an estimator of model is derived following the Booth's linear model estimation procedure. Since polynomial model is nonlinear function of the unknown regression coefficients and error-free predictors, it is nonlinear model in errors-in-variables model. As a result of applying linear model estimation method to nonlinear model, some additional assumptions are necessary. Hence, an estimator is derived under the assumption that the error variances are decrasing as sample size increases. Asymptotic propoerties of the derived estimator are provided. A simulation study is presented to compare the small sample properties of the derived estimator with those of OLS estimator.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.117-124
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• 2015

As the acceptance of statistical analysis has been increased because of Big Data, the needs for an advanced second generation of statistical analysis method like Structural Equation Model are also increasing. This study suggests how R-Program, as open software, can be utilized when Partial Least Square Model, one of the SEMs, is applied to statistical analysis. R is a free software as a part of GNU projects as well as a powerful and useful tool for statistical analysis including Big Data. The study utilized R and SmartPLS, a representative statistical package of PLS-SEM, and analyzed internal consistency reliability, convergent validity, and discriminant validity of the measurement model. The study also analyzed path coefficients and moderator effects of the structural model and compared the results, respectively. The results indicated that R showed the same results with SmartPLS on the measurement model and the structural model. Therefore, the study confirmed that R could be a powerful tool that is alternative to a commercial statistical package in the future.