• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.185-191
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• 2011

In the credit evaluation study with the assumption of mixture distributions, the ROC curve is a useful method to explore the discriminatory power of default and non-default borrowers. The AROC curve is an adjusted ROC curve that can be identified with the corresponding score and is mathematically analyzed in this work. We obtain patterns of this curve by applying normal distributions. Moreover, the relationship between the AROC curve and many classification accuracy statistics are explored to find the optimal threshold. In the case of equivalent variances of two distributions, we obtain that the local minimum of the AROC curve is estimated at the optimal threshold to maximize certain classification accuracies.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.343-352
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• 2000

In this study. the author proposes a nonhierarchical clustering method. called the "Double K-Means Clustering", which performs clustering of multivariate observations with the following algorithm: Step I: Carry out the ordinary K-means clmitering and obtain k temporary clusters with sizes $n_1$,... , $n_k$, centroids $c_$1,..., $c_k$ and pooled covariance matrix S. $\bullet$ Step II-I: Allocate the observation x, to the cluster F if it satisfies ..... where N is the total number of observations, for -i = 1, . ,N. $\bullet$ Step II-2: Update cluster sizes $n_1$,... , $n_k$, centroids $c_$1,..., $c_k$ and pooled covariance matrix S. $\bullet$ Step II-3: Repeat Steps II-I and II-2 until the change becomes negligible. The double K-means clustering is nearly "optimal" under the mixture of k multivariate normal distributions with the common covariance matrix. Also, it is nearly affine invariant, with the data-analytic implication that variable standardizations are not that required. The method is numerically demonstrated on Fisher's iris data.

• Journal of KIISE:Software and Applications
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• v.35 no.7
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• pp.417-423
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• 2008

In this paper we present a probabilistic method for source separation in the case here each source has a certain temporal structure. We tackle the problem of source separation by maximum pseudo-likelihood estimation, representing the latent function which characterizes the temporal structure of each source by a random process with a Gaussian prior. The resulting pseudo-likelihood of the data is Gaussian, determined by a mixing matrix as well as by the predictive mean and covariance matrix that can easily be computed by Gaussian process (GP) regression. Gradient-based optimization is applied to estimate the demixing matrix through maximizing the log-pseudo-likelihood of the data. umerical experiments confirm the useful behavior of our method, compared to existing source separation methods.

• Journal of the Korean Geotechnical Society
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• v.16 no.5
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• pp.179-191
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• 2000

본 연구에서는 화강토 지반상의 자립식 diaphragm wall의 거동을 연구하기 위하여 벽체의 근입깊이비, 지하수위 및 굴착조건(연속 및 단계굴착)을 변화시키면서 원심모형시럼을 수행하였다. 원심모형실험시 지반굴착은 흙과 동일한 밀도로 혼합된 zine chloride 용액이 배수되도록 밸브를 조작하여 실시하였으며, 굴착에 의해 발생되는 지반의 변형괴 벽체의 변위 및 휨모멘트를 시간경과에 따라 측정하였다. 실험결과, 벽체의 근입깊이비가 증가함에 따라 벽체의 휨모멘트는 증가하는 반면, 굴착과정동안 배면측에서의 간극수압 감소속도는 감소하였다. 최종 굴착단계에서 굴착후 시간경과에 따른 침하량은 굴착과정중의 침하？에 비해 5~7% 정도를 나타내었다. 최대표면침하량과 벽체변위를 굴착깊이로 정규화한 결과 최대 침하량은 벽체 변위량의 0.8~1.2배9평균0.91배)사이에 분포하였다. 굴착깊이로 전규화한 벽체변위와 근입깊이와의 관계는 지수함수식으로 제안하였다. 파괴면은 직선적인 형태로 파괴면내의 배면측 지반은 벽체를 향하여 하향의 변위를 일으키면서 벽체의 회전에 의해 파괴되었으며, 퐈괴면의 각도는 66~72.5$^{\circ}$정도로 이론적인 파괴면의 각도보다 크게 평가되었다.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.665-682
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• 2020

This paper introduces numba library in Python, which improves computational efficiency of the provided implemented code written by naive Python language by applying just-in-time (JIT) compilation. To apply just-in-time compilation, the numba only needs to use a decorator on a target Python function. We provide implementation examples with numba for the permutation test and the parameter estimation for Gaussian mixture distribution. We also numerically show the efficiency of numba by comparing the total computation times of the implementation using naive python and the implementation using numba for each application.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.27 no.3
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• pp.170-177
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• 1991

This paper presents the seasonal variation of distribution and flushing time of the fresh water in Suyoung Bay based on the monthly observation from May 1989 through April 1990 and Pusan City Report of Suyoung Bay. Most of Suyoung river water was trapped inside of the bay west of the Dong-Baek Island located. Low salinity water lies dominantly on the right hand side of the Suyong river. Salinity structure of the bay is the well mixed type in summer and the partially mixed type in other season. The fresh water fraction varied in an exponetial manner from unity at the head of bay toward a value of zero at the its mouth. The calculated average flushing time during a year was about 10-15days. About 1.5 days was in summer because the strong fresh water discharge from the river was dominated in the bay.

• 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 Data and Information Science Society
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• v.28 no.5
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• pp.959-970
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• 2017

In this study, we propose bivariate skewness and kurtosis statistics and suggest a surface plot that can visually implement bivariate data containing the correlation coefficient. The skewness statistic is expressed in the form of a paired real values because this represents the skewed directions and degrees of the bivariate random sample. The kurtosis has a positive value which can determine how thick the tail part of the data is compared to the bivariate normal distribution. Moreover, the surface plot implements bivariate data based on the quantile vectors. Skewness and kurtosis are obtained and surface plots are explored for various types of bivariate data. With these results, it has been found that the values of the skewness and kurtosis reflect the characteristics of the bivariate data implemented by the surface plots. Therefore, the skewness, kurtosis and surface plot proposed in this paper could be used as one of valuable descriptive statistical methods for analyzing bivariate distributions.

• Journal of Preventive Medicine and Public Health
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• v.31 no.1 s.60
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• pp.104-111
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• 1998

To obtain the basic information that can be used as a factor for explaining the diversity of welders' pneumoconiosis, the authors measured the concentrations of dust according to the size of dust in 71 workplaces of a shipyard where welders' pneumoconiosis have occurred. The concentrations of dust according to the size of dust showed no difference between workplaces regardless of kinds of work.

• Journal of the Korea Computer Graphics Society
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• v.21 no.3
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• pp.27-35
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• 2015

This study presents a volume rendering method using ambient occlusion which is one of global illumination methods. By considering the volume density distribution as normal distribution, ambient occlusion can be calculated at real-time speed regardless of modification of opacity transfer function. We calculate and store the averages and standard deviations of densities in a block centered at each voxel in pre-processing time. In rendering process, we determine the illumination value by estimating the nearby opacity. We generalized theoretical model and generated better quality images improving our previous research. In detail, various shapes of transfer function can be used due to the proposed equation model. Moreover, we introduced a multi-range model to give nearer objects more weight. As the result, more realistic volume rendering image can be generated at real-time speed by mixing local and ambient occlusion shading.