• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.425-433
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• 2011

Reject inference in credit scoring is a statistical approach to adjust for nonrandom sample bias due to rejected applicants. Function estimation approaches are based on the assumption that rejected applicants are not necessary to be included in the estimation, when the missing data mechanism is missing at random. On the other hand, the density estimation approach by using mixture models indicates that reject inference should include rejected applicants in the model. When mixture models are chosen for reject inference, it is often assumed that data follow a normal distribution. If data include missing values, an application of the normal mixture model to fully observed cases may cause another sample bias due to missing values. We extend reject inference by a multivariate normal mixture model to handle incomplete characteristic variables. A simulation study shows that inclusion of incomplete characteristic variables outperforms the function estimation approaches.

• Journal of the Korean Solar Energy Society
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• v.33 no.2
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• pp.18-27
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• 2013

This paper presents a method for assessing the wind energy potential at complex terrain using probability distribution. And the proper probability models of the parameters estimating the wind energy are presented. Finally a mixture-Weibull determined by numerical methods procedure are proposed to assess the probability distribution of the energy potential at a site. The developed method is applied to the Kwanjungchun Bridge and compared with wind records which the neighboring weather station.

• The Korean Journal of Applied Statistics
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• v.21 no.5
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• pp.835-843
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• 2008

In this paper, we investigate a Bayesian inference for software reliability models based on mean value functions which take the form of the mixture of beta distribution functions. The posterior simulation via the Markov chain Monte Carlo approach is used to produce estimates of posterior properties. Its applicability is illustrated with two real data sets. We compute the predictive distribution and the marginal likelihood of various models to compare the performance of them. The model comparison results show that the model based on the beta-mixture performs better than other models.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.246-249
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• 2009

Due to the additive white Gaussian noise (AWGN), images are often corrupted. In recent days, Bayesian estimation techniques to recover noisy images in the wavelet domain have been studied. The probability density function (PDF) of an image in wavelet domain can be described using highly-sharp head and long-tailed shapes. If a priori probability density function having the above properties would be applied well adaptively, better results could be obtained. There were some frequently proposed PDFs such as Gaussian, Laplace distributions, and so on. These functions model the wavelet coefficients satisfactorily and have its own of characteristics. In this paper, mixture distributions of Gaussian and Laplace distribution are proposed, which attempt to corporate these distributions' merits. Such mixture model will be used to remove the noise in images by adopting Maximum a Posteriori (MAP) estimation method. With respect to visual quality, numerical performance and computational complexity, the proposed technique gained better results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.194-202
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• 2019

Reliability analysis of the components frequently starts with the data that manufacturer provides. If enough failure data are collected from the field operations, the reliability should be recomputed and updated on the basis of the field failure data. However, when the failure time record for a component contains only a few observations, all statistical methodologies are limited. In this case, where the failure records for multiple number of identical components are available, a valid alternative is combining all the data from each component into one data set with enough sample size and utilizing the useful information in the censored data. The ROK Navy has been operating multiple Patrol Killer Guided missiles (PKGs) for several years. The Korea Multi-Function Control Console (KMFCC) is one of key components in PKG combat system. The maintenance record for the KMFCC contains less than ten failure observations and a censored datum. This paper proposes a Bayesian approach with a Dirichlet mixture model to estimate failure time density for KMFCC. Trends test for each component record indicated that null hypothesis, that failure occurrence is renewal process, is not rejected. Since the KMFCCs have been functioning under different operating environment, the failure time distribution may be a composition of a number of unknown distributions, i.e. a mixture distribution, rather than a single distribution. The Dirichlet mixture model was coded as probabilistic programming in Python using PyMC3. Then Markov Chain Monte Carlo (MCMC) sampling technique employed in PyMC3 probabilistically estimated the parameters' posterior distribution through the Dirichlet mixture model. The simulation results revealed that the mixture models provide superior fits to the combined data set over single models.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.131-140
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• 1994

In order to find the identification function from data and to apply the identification function for the pattern classification, we consider the existing problem of the number of patterns in such data. In this paper, a new criteria for the identification of Gaussaian mixture distribution could be established as a charateristic of the sample variance, which is a bootstrap estimate of the sample variance. We examine the properties and fittness of the criteria through a large scale of computer simulations.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.517-529
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• 2016

This study compares the inverse transform and the composition methods for generating data from the Lindley distribution. The expression for the inverse of the distribution function for the Lindley distribution does not exist in closed form. Hence, authors of many empirical studies on the Lindley distribution used methods for generating Lindley variates other than the inverse transform. We generated data from the Lindley distribution using the inverse transform approach by obtaining the Lindley variates numerically; we also generated data from this distribution using the composition approach. Following the generation of the Lindley variates using these two methods, we compare some statistical properties of the estimates of the Lindley model parameters based on the generated data. We conclude that the two methods produce similar results.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.2
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• pp.146-150
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• 2010

Distribution Function and Drift velocities for electrons in $SF_6$-Ar mixtures gases used by MCS-BEq algorithm has been analysed over the E/N range 30~300[Td] by a two term Boltzmann equation and by a Monte Carlo simulation using a set of electron cross sections determined by other authors, experimentally the electron swarm parameters for 0.2[%] and 0.5[%] $SF_6$-Ar mixtures were measured by time-of-flight method. The results obtained in this work will provide valuable information on the fundamental behaviors of electrons in weakly ionized gases and the role of electron attachment in the choice of better gases and unitary gas dielectrics or electro negative components in dielectric gas mixtures. The results show that the deduced electron drift velocities agree reasonably well with theoretical for a rang of E/N values.

• ETRI Journal
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• v.33 no.6
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• pp.949-952
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• 2011

In this letter, a simplified suboptimum receiver based on soft-limiting for the detection of binary antipodal signals in non-Gaussian noise modeled as a generalized normal-Laplace (GNL) distribution combined with Gaussian noise is presented. The suboptimum receiver has low computational complexity. Furthermore, when the number of diversity branches is small, its performance is very close to that of the Neyman-Pearson optimum receiver based on the probability density function obtained by the Fourier inversion of the characteristic function of the GNL-plus-Gaussian distribution.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1326-1328
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• 2003

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture -density distributions in the feature space, the proposed network not only possesses the advantage of the RBF mechanism but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is faster in training, more accurate, and simpler in structure.