• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.871-883
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• 2015

Vector autoregressive (VAR) models in high dimension suffer from noisy estimates, unstable predictions and hard interpretation. Consequently, the sparse vector autoregressive (sVAR) model, which forces many small coefficients in VAR to exactly zero, has been suggested and proven effective for the modeling of high dimensional time series data. This paper studies coupling measures to select non-zero coefficients in sVAR. The basic idea based on the simulation study reveals that removing the effect of other variables greatly improves the performance of coupling measures. sVAR model coefficients are asymmetric; therefore, asymmetric coupling measures such as Granger causality improve computational costs. We propose two asymmetric coupling measures, filtered-cross-correlation and filtered-Granger-causality, based on the filtered residuals series. Our proposed coupling measures are proven adequate for heavy-tailed and high order sVAR models in the simulation study.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.833-845
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• 2011

This paper conducts a statistical analysis of extreme values for both daily log-returns and daily negative log-returns, which are computed using a collection of KOSPI data from January 3, 1998 to August 31, 2011. The Poisson-GPD model is used as a statistical analysis model for extreme values and the maximum likelihood method is applied for the estimation of parameters and extreme quantiles. To the Poisson-GPD model is also added the Bayesian method that assumes the usual noninformative prior distribution for the parameters, where the Markov chain Monte Carlo method is applied for the estimation of parameters and extreme quantiles. According to this analysis, both the maximum likelihood method and the Bayesian method form the same conclusion that the distribution of the log-returns has a shorter right tail than the normal distribution, but that the distribution of the negative log-returns has a heavier right tail than the normal distribution. An advantage of using the Bayesian method in extreme value analysis is that there is nothing to worry about the classical asymptotic properties of the maximum likelihood estimators even when the regularity conditions are not satisfied, and that in prediction it is effective to reflect the uncertainties from both the parameters and a future observation.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.29-43
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• 2012

Financial data such as stock index returns and exchange rates have the properties of heavy tail and asymmetry compared to normal distribution. When we estimate VaR using the GARCH model (with the conditional return distribution of normal) it shows the tendency of the lower estimation and clustering in the losses over the estimated VaR. In this paper, we argue that this problem can be resolved through the adaptation of the unbounded Johnson distribution as that of the condition return. We also compare this model with the GARCH with the conditional return distribution of normal and student-t. Using the losses exceed the ex-ante VaR, estimates, we check the validity of the GARCH models through the failure proportion test and the clustering test. We nd that the GARCH model with conditional return distribution of unbounded Johnson provides an appropriate estimation of the VaR and does not occur the clustering of violations.

• Journal of Korea Port Economic Association
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• v.26 no.2
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• pp.139-149
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• 2010

This paper analyses the relative efficiency of 13 container terminals based on the data for the period 2003-8 to offer a fresh perspective. There has been abundant empirical research undertaken on the technical efficiency of Busan and Gwangyang port. Most studies have focused on the use of parametric and non-parametric techniques to analyse overall technical efficiency. Here, the framework assumes that terminals use two input to produce one output; the former includes container yard and container crane and the latter container volume. Jarque-Bera indicates that three variables are not normally distributed and the positive skewness shows that all the variables have long right tails. This means there are many small-scaled container terminals. This paper also employs heteroscedastic Tobit model to show the effect of the explanatory variables on the container terminal efficiencies. The Tobit model shows that both container yard and container cranes have positive effect on the container terminal efficiency, but container yard has a higher impact on the efficiency than the container crane.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.11 no.1
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• pp.20-33
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• 1999

The evolution of deep-sea waves is driven by energy input from wind, nonlinear energy transfer between wave components, and dissipation through whitecaps. A comparative study was implemented by the use of two wave models in which only the computation methods of nonlinear wave-wave interactions are different from each other. It was reaffirmed that the nonlinear interaction plays a central role in such phenomena that occurred during the spectral growth of wind-seas as down-shift of the spectral peak frequency, overshoot, undershoot, and formation of self-similar spectrum. Specifically, the directional distribution at high frequencies develops into bimodal form, which is attributed to the nonlinear interactions. As saturation stage is reached, spectral density at high frequencies becomes proportional to negative 4 power to the frequency. Perturbations introduced into the spectrum quickly vanished through the actions of the self-similar mechanism. Thus, the nonlinear transfer has important contribution to the stability of numerical ocean wave models.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.567-575
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• 2016

With the popularity of smartphone, user's location information is of great interest as mobile apps based on the location information are increasing. In this paper, we are interested in analyzing user's speed data based on the location information. It is not uncommon to observe locations with great measurement errors, removing them is necessary. The distribution of speed can be considered as a mixture model in accordance with transportation means. We identify a tail part as a component of a mixture model and fit a simple parametric model to the tail part of the speed distribution.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.459-466
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• 2011

This article addresses robust Bayesian modeling for meta analysis which derives general conclusion by combining independently performed individual studies. Specifically, we propose hierarchical Bayesian models with unknown variances for meta analysis under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. For the numerical analysis, we use the Gibbs sampler for calculating Bayesian estimators and illustrate the proposed methods using actual data.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.101-115
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• 2003

Lately there has been much theoretical and applied interest in linear models with non-normal heavy tailed error distributions. Starting Zellner(1976)'s study, many authors have explored the consequences of non-normality and heavy-tailed error distributions. We consider hierarchical models including selection models under a skewed heavy-tailed e..o. distribution proposed originally by Chen, Dey and Shao(1999) and Branco and Dey(2001) with Dirichlet process prior(Ferguson, 1973) in order to use a meta-analysis. A general calss of skewed elliptical distribution is reviewed and developed. Also, we consider the detail computational scheme under skew normal and skew t distribution using MCMC method. Finally, we introduce one example from Johnson(1993)'s real data and apply our proposed methodology.

• Environmental and Resource Economics Review
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• v.27 no.1
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• pp.195-214
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• 2018

This study estimates the price elasticity of residential water demand in Korea. For that, annual panel data from the year of 2010 to 2013 for 161 local water services is estimated by using panel quantile model. As a result, the price elasticities of residental water demand in Korea are estimated to be between -0.156 and -0.189 depending on its quantile. In addition, the study finds that the estimated elasticity of residential water demand by traditional conditional mean regression is relatively more influenced by high demand areas because the distribution of residental water demand in Korea is left-skewed.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.119-131
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• 1995

Based on Monte-Carlo simulation results we propose a robust analysis of variance procedure by utilizing trimmed mean and Winsorized variance. We deal with mainly the one-way classification case. We evaluate the empirical distribution of a pseudo-F statistic based on symmetrically Winsorized sum of squares when the population is normally distributed.