• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.947-958
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• 2014

Understanding extreme precipitation events is very important for flood planning purposes. Especially, the r-year return level is a common measure of extreme events. In this paper, we present a spatial analysis of precipitation return level using hierarchical Bayesian modeling. For intensity, we model annual maximum daily precipitations and daily precipitation above a high threshold at 62 stations in Korea with generalized extreme value(GEV) and generalized Pareto distribution(GPD), respectively. The spatial dependence among return levels is incorporated to the model through a latent Gaussian process of the GEV and GPD model parameters. We apply the proposed model to precipitation data collected at 62 stations in Korea from 1973 to 2011.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.835-844
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• 2010

Tools for statistical analysis of extreme values include the classical annual maximum method, the modern threshold method and variants improving the second one. While the annual maximum method is to t th generalized extreme value distribution to the annual maxima of a time series, the threshold method is to the generalized Pareto distribution to the excesses over a high threshold from the series. In this paper we deal with the Poisson-GPD method, a variant of the threshold method with a further assumption that the total number of exceedances follows the Poisson distribution, and apply it to the daily percentage increases and decreases computed from the spot prices of West Texas Intermediate, which were collected from January 4th, 1988 until December 31st, 2009. According to this analysis, the distribution of daily percentage increases as well as decreases turns out to have a heavy tail, unlike the normal distribution, which coincides well with the general phenomenon appearing in the analysis of lots of nowaday nancial data.

• Parasites, Hosts and Diseases
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• v.38 no.1
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• pp.45-48
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• 2000

An enzyme analysis of the liver fluke, Clonorchis sinensis from Kimhae, Korea and from Shenyang, China was conducted using a horizontal. starch gel electrophoresis in order to elucidate their genetic relationships. A total of eight enzymes was employed from two different kinds of buffer systems. Two loci from each enzyme of aconitase and esterase (${\alpha}-Na{\;}and{\;}{\beta}-Na$) : and only one locus each from six enzymes, gluucose-6-phosphate dehydrogenase (G6PD), ${\alpha}-glycerophosphate$ dehydrogenase (GPD), 3-hydroxybutyrate dehydrogenase (HBDH), malate dehydrogenase (MDH), phosphoglucose isomerase (PGI), and phosphoglucomutase (PGM) were detected. Most of loci in two populations of C. sinensis showed homozygous monomorphic banding patterns and one of them, GPD was specific as genetic markers between two different populations. However, esterase (${\alpha}-Na$), GPD, HBDH and PGI loci showed polymorphic banding patterns. Two populations of C. sinensis were more closely clustered within the range of genetic identity value of 0.998-1.0. In summarizing the above results, two populations of C. sinensis employed in this study showed mostly monomorphic enzyme protein banding patterns, and genetic differences specific between two populations.

• Microbiology and Biotechnology Letters
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• v.34 no.3
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• pp.211-215
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• 2006

AfsR2 is a global regulatory protein involved in the stimulation of secondary metabolite biosynthesis in various Streptomyces species including avermectin-producing S. avermitilis. Among several AfsR2-dependent genes identified from the comparative proteomics, the polyribonucleotide nucleotidyltransferase (PNP) and the glyceraldehyde-3-phosphate dehydrogenase (GPD) genes were previously proposed to regulate the actinorhodin production in S. lividans upon afsR2 over-expression positively and negatively, respectively. To show the biological significance of the PNP and GPD genes in the S. avermitilis strains, these two genes were functionally expressed in both the wild-type and the avermectin-overproducing mutant strains. The PNP gene expression stimulated secondary metabolite production in the wild-type S. avermitilis ATCC31267, but not in the avermectin-overproducing S. avermitilis ATCC31780. Interestingly, the GDP gene expression stimulated secondary metabolite production by 4-fold in the wild-type S. avermitilis ATCC31267 and by 2.5-fold in the avermectin-overproducing S. avermitilis ATCC31780, respectively. These results suggest that the biological significance of the afsR2-dependent PNP and GPD gene expressions on antibiotic biosynthetic regulation could be significantly different depending on Streptomyces species.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.483-494
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• 2013

The importance of financial risk management has been highlighted after several recent incidences of global financial crisis. One of the issues in financial risk management is how to measure the risk; currently, the most widely used risk measure is the Value at Risk(VaR). We can consider to estimate VaR using extreme value theory if the financial data have heavy tails as the recent market trend. In this paper, we study estimations of VaR using Peaks over Threshold(POT), which is a common method of modeling fat-tailed data using extreme value theory. To use POT, we first estimate parameters of the Generalized Pareto Distribution(GPD). Here, we compare three different methods of estimating parameters of GPD by comparing the performance of the estimated VaR based on KOSPI 5 minute-data. In addition, we simulate data from normal inverse Gaussian distributions and examine two parameter estimation methods of GPD. We find that the recent methods of parameter estimation of GPD work better than the maximum likelihood estimation when the kurtosis of the return distribution of KOSPI is very high and the simulation experiment shows similar results.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1E
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• pp.64-70
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• 1994

Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. But those models suffer from poor discrimination between acoustically similar words. In this paper we propose an discriminative training algorithm for predictive neural network models. This algorithm is derived from GPD (Generalized Probabilistic Descent) algorithm coupled with MCEF(Minimum Classification Error Formulation). It allows direct minimization of a recognition error rate. Evaluation of our training algoritym on ten Korean digits shows its effectiveness by 30% reduction of recognition error.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.997-1002
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• 1994

Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. Those models can effectively normalize the temporal and spatial variability of speech signals. But those models suffer from poor discrimination between acoustically similar words. In this paper, we propose a discriminative training algorithm for predictive neural network models based on a generalized probabilistic descent (GPD) algorithm and minimum classification error formulation (MCEF). The Evaluation of our training algorithm on ten Korean digits shows its effectiveness by 40% reduction of recognition error.

• Journal of Veterinary Clinics
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• v.15 no.1
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• pp.116-123
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• 1998

고양이 말초혈액 탐식세포(단핵구세포(MNC) 및 다형핵백혈구(PMNC))의 탐식능 에 있어서 인삼 saponin(ginseng total saponin(G75), ginseng PT saponin(GPT) 및 ginseng PD saponin(GPD))의 면역증강 효과를 flow cytometry를 이용하여 분석하였다. 인삼 ssponins을 직접 첨가하여 배양한 MNC 및 PMNC에서는 탐식중강 효과가 나타나지 않았다. 각각의 인삼 saponin을 첨가하여 배양한 PMNC 및 MNC 배양상충액의 존재하에 PMNC 및 MNC의 탐식능을 겅토한 결과, MNC의 탐식능은 Gff 첨가 PMNC 배양상충액과 GTS 및 GPT 첨가 MNC 배양상충액의 존재하에서 약간의 탐식증강 효과를 보였다. PMNC 탐식능의 경우에는 GPD 첨가 PMNC 배양상충액에서 미약한 탐식증강 효과가 나타났으나, 각각의 인 삼 saponin 첨가 MNC 배양상충액 존재하에서는 모두 현저한 탐식중강 효과를 나타내었다. 이상의 결과로부터 고양이 말초혈액 탐식세포의 탐식증강 효과는 인삼 saponin의 직접적인 작용보다는 인삼 saponin에 의해 활성화된 단핵구세포에서 분비되는 가용성물질에 의해 단 핵구세포보다는 다형핵백혈구에서 현저하게 탐식효과가 증강되는 것으로 판단되었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.453-453
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• 2017

본 연구에서는 최근 기후변화로 인한 집중호우의 발생횟수의 경향을 확률적으로 분석함에 있어 1개월 동안 80 mm/day 이상의 강우사상을 집중호우로 정의하여, 대구 및 부산 강우관측소로부터 수집된 384개월 동안의 집중호우를 분석하였다. 집중호우 월별 발생횟수와 같은 형식의 자료의 확률적 분석은 대개 Poisson 분포 (POI)가 사용되나 자료에 포함된 0자료의 과잉은 확률분포를 왜곡시키는 문제를 발생시킨다. 본 연구에서는 이 문제를 개선하기 위하여 개발된 일반화 Poisson 확률분포 (GPD), 0-과잉 Poisson 확률분포 (ZIP), 0-과잉 일반화 Poisson 확률분포 (ZIGP), Bayesian 0-과잉 일반화 Poisson 확률분포 (Bayesian ZIGP)를 집중호우 자료에 적용하고, 5개 모형의 특성을 비교분석하였으며, Bayesian ZIGP 모형의 구축에 있어서는 정보적 사전분포를 사용함으로써 모형의 정확도를 개선하였다. 분석결과 분석하고자 하는 자료에 0이 과다하게 포함되어 있는 경우 POI 및 GPD 분포는 관측결과와는 다른 결과를 제시하여 적절한 모형으로 고려되지 못함을 알 수 있었다. 5가지 모형 중 정보적 사전분포를 탑재한 Bayesian ZIGP 모형이 가장 관측 자료와 유사한 결과를 도출하였으나 모형의 구축에 수반되는 실용적인 측면을 고려하면 ZIP 모형도 충분히 사용될 수 있는 모형으로 추천되었다.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.101-114
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• 2012

In car insurance, the loss ratio is the ratio of total losses paid out in claims divided by the total earned premiums. In order to minimize the loss to the insurance company, estimating extreme quantiles of loss ratio distribution is necessary because the loss ratio has essential prot and loss information. Like other types of insurance related datasets, the distribution of the loss ratio has heavy-tailed distribution. The Peaks over Threshold(POT) and the Hill estimator are commonly used to estimate extreme quantiles for heavy-tailed distribution. This article compares and analyzes the performances of various kinds of parameter estimating methods by using a simulation and the real loss ratio of car insurance data. In addition, we estimate extreme quantiles using the Hill estimator. As a result, the simulation and the loss ratio data applications demonstrate that the POT method estimates quantiles more accurately than the Hill estimation method in most cases. Moreover, MLE, Zhang, NLS-2 methods show the best performances among the methods of the GPD parameters estimation.