• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.410-418
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• 2007

This article suggests the methods to investigate adverse movement across global stock markets arising from insolvency of subprime mortgage in U.S. Our application deals with asymptotic tail dependence of daily stock index returns (KOSPI, DJIA, Shanghai Composite) of three countries; Korea, U.S., and China, over specific period via extreme value theory and copula functions. Daily stock index returns among three countries show higher extremal dependence during the period exposed to systematic shock. We confirm that extreme value theory and copula functions have potential to well describe the extreme dependence between three countries' daily stock index returns.

• Wind and Structures
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• v.25 no.3
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• pp.261-282
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• 2017

The probabilistic information of directional extreme wind speeds is important for precisely estimating the design wind loads on structures. A new joint probability distribution model of directional extreme wind speeds is established based on observed wind-speed data using multivariate extreme value theory with the t-Copula function in the present study. At first, the theoretical deficiencies of the Gaussian-Copula and Gumbel-Copula models proposed by previous researchers for the joint probability distribution of directional extreme wind speeds are analysed. Then, the t-Copula model is adopted to solve this deficiency. Next, these three types of Copula models are discussed and evaluated with Spearman's rho, the parametric bootstrap test and the selection criteria based on the empirical Copula. Finally, the extreme wind speeds for a given return period are predicted by the t-Copula model with observed wind-speed records from several areas and the influence of dependence among directional extreme wind speeds on the predicted results is discussed.

• Honam Mathematical Journal
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• v.23 no.1
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• pp.159-178
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• 2001

In this paper we introduce a new concept of positive orthant dependence of multivariate stochastic processes. This concept is weaker than the POD but it enjoys most of the properties and preservation results of the POD. Some examples are presented.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.845-858
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• 2000

In this paper new results are obtained for multivariate processes which help us to identify weak negative orthant dependent(WNOD) structures among hitting times of the processes. Furthermore, an approach to derive dependence properties among the processes is proposed and a partial solution to the question tat what kinds of the dependence properties, when they are imposed on processes, are reflected as analogous properties of corresponding hitting times is give. Examples are given to illustrate these concepts.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.113-122
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• 2021

This study aims to investigate the effects of fiscal policy instruments on economic growth in Jordan using annual data from 1970 to 2019, by applying the VAR model (Vector Auto regression) and the Vector Error Correction Model (VECM). The study also examines the dynamic relationship among economic variables over time using the Granger casualty test, Impulse Response Function, and Variance Decomposition. The results show that not only the public expenditures have a positive effect on economic growth in Jordan, but also the tax revenues positively affect the economic growth in the short-run, and this is because of using the tax revenues to finance the government activities in Jordan. This effect becomes negative in the long run, and this is explained because the tax seems a source of distortions in the economy, The extreme taxes may cause huge distortions in the economy, and these distortions destroys the purchasing power, the aggregate demand, and supply. More governmental dependence on tax revenues is the main source of tax evasion and less efficiency. The effect of taxation will curb any prosperity in the economy. Therefore, the government should estimate the fair tax rates to generate sufficient revenues to finance the public expenditure required to enhance economic prosperity.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Journal of Astronomy and Space Sciences
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• v.31 no.2
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• pp.149-157
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• 2014

As the prediction of geomagnetic storms is becoming an important and practical problem, conditions in the Earth's magnetosphere have been studied rigorously in terms of those in the interplanetary space. Another approach to space weather forecast is to deal with it as a probabilistic geomagnetic storm forecasting problem. In this study, we carry out detailed statistical analysis of solar wind parameters and geomagnetic indices examining the dependence of the distribution on the solar cycle and annual variations. Our main findings are as follows: (1) The distribution of parameters obtained via the superimposed epoch method follows the Gaussian distribution. (2) When solar activity is at its maximum the mean value of the distribution is shifted to the direction indicating the intense environment. Furthermore, the width of the distribution becomes wider at its maximum than at its minimum so that more extreme case can be expected. (3) The distribution of some certain heliospheric parameters is less sensitive to the phase of the solar cycle and annual variations. (4) The distribution of the eastward component of the interplanetary electric field BV and the solar wind driving function BV2, however, appears to be all dependent on the solar maximum/minimum, the descending/ascending phases of the solar cycle and the equinoxes/solstices. (5) The distribution of the AE index and the Dst index shares statistical features closely with BV and $BV^2$ compared with other heliospheric parameters. In this sense, BV and $BV^2$ are more robust proxies of the geomagnetic storm. We conclude by pointing out that our results allow us to step forward in providing the occurrence probability of geomagnetic storms for space weather and physical modeling.