• Journal of Korea Water Resources Association
• /
• v.41 no.5
• /
• pp.517-525
• /
• 2008

Regional rainfall quantile depends on the identification of hydrologically homogeneous regions. Various variables relevant to precipitation can be used to form regions. Since the type and number of variables may lead to improve the efficiency of partitioning, it is important to select those precipitation related variables, which represent most of the information from all candidate variables. Multivariate analysis techniques can be used for this purpose. Procrustes analysis which can decrease the dimension of variables based on their correlations, are applied in this study. 42 rainfall related variables are decreased into 21 ones by Procrustes analysis. Factor analysis is applied to those selected variables and then 5 factors are extracted. Fuzzy-c means technique classifies 68 stations into 6 regions. As a result, the GEV distributions are fitted to 6 regions while the lognormal and generalized logistic distributions are fitted to 5 regions. For the comparison purpose with previous results, rainfall quantiles based on generalized logistic distribution are estimated by at-site frequency analysis, index flood method, and regional shape estimation method.

• The Korean Journal of Financial Management
• /
• v.26 no.3
• /
• pp.139-169
• /
• 2009

This paper introduces the modified VaR which takes into account the asymmetry and fat-tails of financial asset distribution, and then compares its out-of-sample forecast performance with traditional VaR model such as historical simulation model and Riskmetrics. The empirical tests using stock indices of 6 countries showed that the modified VaR has the best forecast accuracy. At the test of independence, Riskmetrics and GARCH model showed best performances, but the independence was not rejected for the modified VaR. The Monte Carlo simulation using skew t distribution again proved the best forecast performance of the modified VaR. One of many advantages of the modified VaR is that it is appropriate for measuring VaR of the portfolio, because it can reflect not only the linear relationship but also the nonlinear relationship between individual assets of the portfolio through coskewness and cokurtosis. The empirical analysis about decomposing VaR of the portfolio of 6 stock indices confirmed that the component VaR is very useful for the re-allocation of component assets to achieve higher Sharpe ratio and the active risk management.

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

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.4
• /
• pp.797-810
• /
• 2017

The Korean peninsula is exposed to typhoons every year. Typhoons cause huge socioeconomic damage because tropical cyclones tend to occur with strong winds and heavy precipitation. In order to understand the complex dependence structure between strong winds and heavy precipitation, the copula links a set of univariate distributions to a multivariate distribution and has been actively studied in the field of hydrology. In this study, we carried out analysis using data of wind speed and precipitation collected from the weather stations in Busan and Jeju. Log-Normal, Gamma, and Weibull distributions were considered to explain marginal distributions of the copula. Kolmogorov-Smirnov, Cramer-von-Mises, and Anderson-Darling test statistics were employed for testing the goodness-of-fit of marginal distribution. Observed pseudo data were calculated through inverse transformation method for establishing the copula. Elliptical, archimedean, and extreme copula were considered to explain the dependence structure between strong winds and heavy precipitation. In selecting the best copula, we employed the Cramer-von-Mises test and cross-validation. In Busan, precipitation according to average wind speed followed t copula and precipitation just as maximum wind speed adopted Clayton copula. In Jeju, precipitation according to maximum wind speed complied Normal copula and average wind speed as stated in precipitation followed Frank copula and maximum wind speed according to precipitation observed Husler-Reiss copula.

• The Korean Journal of Applied Statistics
• /
• v.19 no.3
• /
• pp.481-504
• /
• 2006

VaR, a tail-related risk measure is now widely used as a tool for a measurement and a management of financial risks. For more accurate measurement of VaR, recently we are particularly concerned about the approach based on extreme value theory rather than the traditional method based on the assumption of normal distribution. However, many studies about the approaches using extreme value theory was done only for the univariate case. In this paper, we discuss portfolio risk measurements with modelling multivariate extreme value distributions by combining copulas and extreme value theory. We also discuss the estimation of ES together with VaR as portfolio risk measures. Finally, we investigate the relative superiority of EVT-copula approach than variance-covariance method through the back-testing of an empirical data.

• Journal of Global Scholars of Marketing Science
• /
• v.17 no.3
• /
• pp.133-151
• /
• 2007

The purpose of this article is to examine the effects of item parceling on the consistency of significance testing of the causal parameters with regard to the relationship between the relevant constructs, as well as the effects of the item parceling on the goodness-of-fit indices of LISREL's general models. Most of the researchers' major purpose of using structural equation modeling (SEM) is to test their research hypotheses associated with the causal parameters. Therefore, we investigated three general models of LISREL, rather than the frequently used confirmatory factor analytic (CFA) models by many other researchers. The results of the study showed that there was a high level of consistency in the calculated test statics of causal parameters between the item-parceled solutions and the item-level solutions, and that the item-parceled solutions had better goodness-of-fit indices, such as GFI, AGFI, CFI, and NFI, than the solutions at the item level. However, in terms of RMSEA, there was no such tendency.

• Journal of Animal Science and Technology
• /
• v.46 no.4
• /
• pp.509-516
• /
• 2004

This study is to investigate the amount of biased estimates for heritability and genetic correlation according to data structure on marbling scores in Korean cattle. Breeding population with 5 generations were simulated by way of selection for carcass weight, Longissimus muscle area and latent values of marbling scores and random mating. Latent variables of marbling scores were categorized into five by the thresholds of 0, I, 2, and 3 SD(DSI) or seven by the thresholds of -2, -1, 0,1I, 2, and 3 SD(DS2). Variance components and genetic pararneters(Heritabilities and Genetic correlations) were estimated by restricted maximum likelihood on multivariate linear mixed animal models and by Gibbs sampling algorithms on multivariate threshold mixed animal models in DS1 and DS2. Simulation was performed for 10 replicates and averages and empirical standard deviation were calculated. Using REML, heritabilitis of marbling score were under-estimated as 0.315 and 0.462 on DS1 and DS2, respectively, with comparison of the pararneter(0.500). Otherwise, using Gibbs sampling in the multivariate threshold animal models, these estimates did not significantly differ to the parameter. Residual correlations of marbling score to other traits were reduced with comparing the parameters when using REML algorithm with assuming linear and normal distribution. This would be due to loss of information and therefore, reduced variation on marbling score. As concluding, genetic variation of marbling would be well defined if liability concepts were adopted on marbling score and implemented threshold mixed model on genetic parameter estimation in Korean cattle.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.6
• /
• pp.2667-2676
• /
• 2013

This study was to examine the relationship between suicidal ideation, depression, stress and their parent's socioeconomic status. Nation-wide representative data from the Korean National Health and Nutrition Examination Survey 2009 were used in this study. 904 adolescents with parent were analysed. Parent's socioeconomic status, especially mother's low educational level and the beneficiaries for national basic livelihood security were significant risk factor for adolescents' suicidal ideation. These associations remained significant in multiple logistic regression controlling for all covariates. The findings in the current study support the global literature on the importance of socioeconomic status in promoting adolescent's mental health. Future prevention intervention efforts to improve adolescent's suicide risk will need to take into consideration parent's and household's socioeconomic conditions. Future study is needed to explore the possible proximal risk factors and mediators between parent's socioeconomic status and mental health among adolescents.

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

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.7 no.2
• /
• pp.91-97
• /
• 1974

Two scale characters, the width ana circuli counts of the first-year band, were used in a discriminant function analysis to see how effectively the two scale characters would separate geographical chum stocks from the western North Pacific. A total of 476 scale samples were taken from spawning adults which ascended to rivers of Hokkaido, Japan, in 1956, and Kamchatka, the U.S.S.R., in 1957. The scale characters were examined for conformity to the statistical requirements of a discriminant function. As a result of the examinations the two characters were verified to be able to be used in a discriminant function analysis that would classify chum taken on the high seas to most Probable origin. A discriminant function computed using the two characters correctly classified 78.5 percent of the Hokkaido and Kamchatka chum fish. Of the two characters the number of the circuli could alone classify fish to its origin with nearly the same probability of correct classification as the discriminant function based on the two characters can.