• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.875-887
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• 2021

Value at Risk (VaR) is the most popular measure for market risk. In this paper, we consider the VaR estimation of portfolio consisting of a variety of assets based on multivariate copula model known as vine copula. In particular, sparse vine copula which penalizes too many parameters is considered. We show in the simulation study that sparsity indeed improves out-of-sample forecasting of VaR. Empirical analysis on 60 KOSPI stocks during the last 5 years also demonstrates that sparse vine copula outperforms regular copula model.

• Survey Research
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• v.12 no.2
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• pp.113-144
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• 2011

The purposes of the present study were (1) to identify the latent classes depending on youth employees' trajectories in job satisfaction and turnover intention and (2) to test the effects of person-job fit(major fit, education level fit, skill level fit) on job satisfaction and turnover intention using Youth Panel 2001. In order to estimate latent classes of job satisfaction and turnover intention changes simultaneously and study probabilities linking latent class membership in trajectory across the two variables, we applied dual trajectory model, an extension of semi-parametric group-based approach, Results showed that four latent classes were identified for job satisfaction, which were defined, based on the trajectory patterns, as increasing group, decreasing group, medium-level group, and high-level group. And, three latent classes estimated for turnover intention were defined as low-level group, maintaining group, and rapidly decreasing group. To test the effects of person-job fit variables, we added the variables as time-dependant variables to the unconditional latent class model. The effect of education level fit and skill level fit were found significant in the groups which are low in job satisfaction and have high in turnover intention. Findings from this study suggest the need to consider trajectory heterogeneity in the study of youth employees' job satisfaction and turnover intention to capture the dynamic dimension of overlap between the two constructs.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.376-380
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• 2011

강우사상은 강우량, 지속기간, 강우강도 등의 특성으로 표현될 수 있으며 이런 인자들을 같이 고려할수록 그 현상을 보다 종합적으로 표현할 수 있다. 하지만 현재 일반적으로 이루어지는 일변량 빈도해석절차에서는 지속기간을 고정시켜놓고 각 지속시간에 따른 결과만을 도출해 낼 수 있기 때문에 지속기간에 대해 제약적이고 입력자료에 존재하지 않는 지속기간에 대한 결과를 얻기가 어렵다. Copula모델은 두 일변량 분포형을 다변량 분포형으로 연결하여 주는 모델이다. 따라서 강우량과 지속기간을 변수로 사용하면 Copula모델을 통한 이변량 강우빈도해석은 보편적으로 이루어지고 있는 일변량 지점빈도해석보다 지속기간에 대해 유연한 결과를 나타낼 수 있다. 즉, 강우와 지속기간이 동시에 변수로 사용되기 때문에 임의의 지속기간이나 강우에 대해서 확률강우량 및 확률지속기간을 얻을 수 있다. 본 연구에서는 서울지점을 대상으로 1961∼2009년 동안 발생한 강우사상 중 각 년도에서 최대강우량이 발생한 사상을 추출하여 입력자료로 사용하였다. Copula 모형은 Gumbel-Hougaard, Frank, Joe, Clayton, Galambos등 총 5개의 모델을 적용하였고 각 Copula의 매개변수는 준모수방법인 maximum pseudolikelihood estimator를 이용하여 추정하였다.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• Korean Business Review
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• v.6
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• pp.201-228
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• 1993

The objective of this research is to investigate the attetudes of religious groups(christian, buddhist, atheist) towards corporate social expenditures. Methodology was a questionnair survey. The data analysis was done by the Kruscal Wallis test and Wilcoxon signed ranks test. The results of this rssearch is that the difference of attitudes between christian group and other groups and between religious groups and atheist group on the social expenditures did not turn out to be statistically significant. The implication of this results is that the variables of religious groups can be ignored when we are going to study on the attitudes of values for the Korean people.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.387-395
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• 2010

It is important to identify the interaction of genes about human disease and characteristic value. Many studies as like logistic analysis, have associated being pursued, but, previous methods did not consider the sub-group of the genotypes. So, QTL interactions and the GMM (genotype matrix mapping) have been developed. In this study, we detect the superior genotype combination to have an impact on economic traits of Korean cattle based on the study over GMM method. Thus, we identified interaction effects of single nucleotide polymorphisms (SNPs) responsible for average daily gain(ADG), marbling score (MS), carcass cold weight (CWT), longissimus muscle dorsiarea (LMA) using GMM method. In addition, we examine significance of the major genotype combination selected by implementing permutation test of the F-measure which was not obtained by Sachiko et al.

• Journal of Embryo Transfer
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• v.22 no.3
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• pp.143-147
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• 2007

Records on 546 Korea native pigs for average daily gain (ADG), age at 70 kg (D70 kg) and backfat thickness (BF) made between 2001 and 2006 in herds on National Institutes of Animal Science in Korea were used to estimate genetic parameters. The data was analyzed by the DF-REML (Derivative-Free Restricted Maximum Likelihood) program of Boldman using a single-trait animal model. Heritabilities were 0.26, 0.09, and 0.29 for ADG, D70 kg and BF, respectively. The phenotypic correlations of ADG with D70 kg and BF were -0.71 and 0.30. The phenotypic correlation of D70 kg with BF was -0.15. The genetic correlations of ADG with D70 kg and BF were -0.11, 0.41, respectively. The genetic correlation of D70 kg with BF was -0.16. The data of weights and measurements on body length, body height and chest width after age at 11 months (days to 330) were shown scarcely less differences compare to data of age at 11 months.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.262-271
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• 2002

This study presents a practical procedure for the Bayesian inversion of geophysical data. We have applied geostatistical techniques for the acquisition of prior model information, then the Markov Chain Monte Carlo (MCMC) method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.163-181
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• 2012

This paper studies the effect of job training for non-regular employees in the Korea labor market. Using an economically active population data set of statistics Korea, we apply a non-parametric matching and sensitivity analysis method to measure the effect of the training for non-regular employees and to look for the impact of an unobservable variable or confounding factor in regards to the selection effect and outcome effect. In the our empirical results, we conclude that the effect of the training for non-regular employees has a better employment effect for getting a regular job rather than a wage effect; in addition, the impact of unobservable variables or confounding factors do not exercise a statistically strong influence on the baseline ATT.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.94-99
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• 2015

With the recent machine learning paradigm of using nonparametric Bayesian statistics and statistical inference based on random sampling, the Dirichlet distribution finds many uses in a variety of graphical models. It is a multivariate generalization of the gamma distribution and is defined on a continuous (K-1)-simplex. This paper presents a sampling method for a Dirichlet distribution for the problem of dividing an integer X into a sequence of K integers which sum to X. The target samples in our problem are all positive integer vectors when multiplied by a given X. They must be sampled from the correspondingly gridded simplex. In this paper we develop a Markov Chain Monte Carlo (MCMC) proposal distribution for the neighborhood grid points on the simplex and then present the complete algorithm based on the Metropolis-Hastings algorithm. The proposed algorithm can be used for the Markov model, HMM, and Semi-Markov model for accurate state-duration modeling. It can also be used for the Gamma-Dirichlet HMM to model q the global-local duration distributions.