• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.749-756
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• 2012

An approach widely used for small area estimation is based on linear mixed models. However, when the functional form of the relationship between the response and the input variables is not linear, it may lead to biased estimators of the small area parameters. In this paper we propose M-quantile kernel regression for small area mean estimation allowing nonlinearities in the relationship between the response and the input variables. Numerical studies are presented that show the sample properties of the proposed estimation method.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.631-638
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• 2008

Accelerated Lifetime Test is a method of estimation of lifetime quality characteristics under operation condition with the accelerated lifetime data obtained under accelerated stress. In this paper we propose estimation method with accelerated lifetime data using quantile regression. We apply the method to real data with Arrhenius and Inverse power model.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.803-812
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• 2009

The application of extreme value theory to financial data is a fairly recent innovation. The classical annual maximum method is to fit the generalized extreme value distribution to the annual maxima of a data series. An alterative modern method, the so-called threshold method, is to fit the generalized Pareto distribution to the excesses over a high threshold from the data series. A more substantial variant is to take the point-process viewpoint of high-level exceedances. That is, the exceedance times and excess values of a high threshold are viewed as a two-dimensional point process whose limiting form is a non-homogeneous Poisson process. In this paper, we apply the two-dimensional non-homogeneous Poisson process model to daily losses, daily negative log-returns, in the data series of KBW/USD exchange rate, collected from January 4th, 1982 until December 31 st, 2008. The main question is how to estimate extreme quantiles of losses such as the 10-year or 50-year return level.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.655-665
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• 2014

An adequate understanding and response to natural hazards such as heat wave, heavy rainfall and severe drought is required. We apply extreme value theory to analyze these abnormal weather phenomena. It is common for extremes in climatic data to be nonstationary in space and time. In this paper, we analyze summer rainfall data in South Korea using exceedance values over thresholds estimated by quantile regression with location information and time as covariates. We group weather stations in South Korea into 5 clusters and t extreme value models to threshold exceedances for each cluster under the assumption of independence in space and time as well as estimates of uncertainty for spatial dependence as proposed in Northrop and Jonathan (2011).

• Management & Information Systems Review
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• v.32 no.1
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• pp.173-194
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• 2013

The purpose of this study drew the core elements of ISO26000 by analyzing the elements suitable to the characteristics of casino companies, and examined the influence of the core elements of ISO26000 on organizational trust following the level of organizational trust of employees. As a result of the factor analysis, among the 7 measurement items of ISO26000, improvement of governance and fair operating practices were simplified into one factor and thus 6 factors were used for empirical analysis. Therefore, multiple regression analysis using least square method was conducted to examine the impacts of the 6 elements. As a result, 5 variables excluding human rights had significant impacts on the organizational trust. Concretely, the 5 core elements of ISO26000 (labor practices, governance and fair operation, consumer issues, environment and community social and economic development) had significant impact on organization trust in order. In addition, the results of quantile regression analysis show the core elements of ISO26000 had different impacts on organizational trust depending on the level of organizational trust of employees.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.233-260
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• 2022

In this paper, we analyzed how oil price fluctuations affect stock price by industry using the non-parametric quantile causality test method. We used weekly data of WTI spot price, KOSPI index, and 22 industrial stock indices from January 1998 to April 2021. The empirical results show that the effect of changes in oil prices on the KOSPI index was not significant, which can be attributed to mixed responses of diverse stock prices in several industries included in the KOSPI index. Looking at the stock price response to oil price by industry, the 9 of 18 industries, including Cloth, Paper, and Medicine show a causality with oil prices, while 9 industries, including Food, Chemical, and Non-metal do not show a causal relationship. Four industries including Medicine and Communication (0.45~0.85), Cloth (0.15~0.45), and Construction (0.5~0.6) show causality with oil prices more than three quantiles consecutively. However, the quantiles in which causality appeared were different for each industry. From the result, we find that the effects of oil price on the stock prices differ significantly by industry, and even in one industry, and the response to oil price changes is different depending on the market situation. This suggests that the government's macroeconomic policies, such as industrial and employment policies, should be performed in consideration of the differences in the effects of oil price fluctuations by industry and market conditions. It also shows that investors have to rebalance their portfolio by industry when oil prices fluctuate.

• Environmental and Resource Economics Review
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• v.31 no.4
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• pp.755-775
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• 2022

This study empirically analyzes that the asymmetry of domestic gasoline price adjustment to the crude oil price changes can vary depending on the level of gasoline price using quantile autoregressive distributed lag model. The data used are the weekly average Dubai price, domestic gasoline price at refiners and gas stations from the first week of May 2008 to the second week of October 2022. The study estimates three price transmission channels: changes in gas station gasoline prices in response to changes in Dubai oil prices, changes in refiners gasoline prices in response to changes in Dubai oil prices, and changes in gas station prices relative to refiners gasoline prices. As a result, the price adjustment of refiner's gasoline price with respect to Dubai oil price appears asymmetrically across all quantiles of gasoline price, whereas the adjustment of gas station prices for Dubai oil price and refiner's gasoline price tend to be more asymmetric as the quantile of gasoline price increases. Such a result is presumed to be due to changes in the inventory cost of gas stations. When the burden of inventory cost is high, gas stations have an incentive to more actively pass the increased buying price on their selling price.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.83-92
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• 2015

Concerning the national health, it is important to develop an accurate prediction method of atmospheric particulate matter (PM) because being exposed to such fine dust can trigger not only respiratory diseases as well as dermatoses, ophthalmopathies and cardiovascular diseases. The National Institute of Environmental Research (NIER) employs a decision tree to predict bad weather days with a high PM concentration. However, the decision tree method (even with the inherent unstableness) cannot be a suitable model to predict bad weather days which represent only 4% of the entire data. In this paper, while presenting the inaccuracy and inappropriateness of the method used by the NIER, we present the utility of a new prediction model which adopts boosting with quantile loss functions. We evaluate the performance of the new method over various ${\tau}$-value's and justify the proposed method through comparison.

• Korean Journal of Clinical Laboratory Science
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• v.48 no.1
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• pp.22-29
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• 2016

Serum gamma-glutamyltransferase (GGT) has been widely used as a marker of alcohol intake and liver failure. Recently, the relativity between GGT and various diseases has been identified with growing interest. In this study, we examined relativity between GGT value and risk factors of coronary heart diseases among those with normal GGT value, excluding heavy drinkers. Specifically, we compared the differences based on age and gender. Data from the 2011 KNHNES were used (N=3,619). When the subjects were categorized according to quartile based on the serum GGT levels, there was 10~20, 21~27, 28~38, 39~71 IU/L in men, and 6~12, 13~16, 17~22, 23~42 IU/L in women. The mean of most variables was the highest in the $4^{th}$ quartile (Q4), however age and LDL Cholesterol were the highest in the $2^{nd}$ quartile (Q2) in men. The FRS and 10-year CHD risk was the highest in the $2^{nd}$ quartile in men, and the highest in the $4^{th}$ quartile in women. Increased GGT was correspondingly linked with age in women but age was the highest in GGT in the $2^{nd}$ quartile in men. In the 70's, the highest Q1 and Q2 was in men and the highest Q3 and Q4 in women. Although GGT value was within the normal range, increased GGT showed correlation with various risk factors. The FRS and 10-year CHD risk showed different patterns according to age and gender along with increased GGT value.

• The Journal of Engineering Geology
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• v.27 no.4
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• pp.367-376
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• 2017

Recently, tunneling under the seabed is becoming increasingly common in many countries. In Korea, there are proposals to tunnel from the mainland to Jeju Island. Safe construction requires geologic structures such as faults to be characterized during the design and construction phase; however, unlike on land, such structures are difficult to survey seabed. This study aims to develop an algorithm that uses geostatistics to automatically derive large-scale geological structures on the seabed. The most important considerations in this method are the optimal size of the moving window, the optimal type of spatial statistics, and determination of the optimal percentile standard. Finally, the optimal analysis algorithm was developed using the R program, which comprehensibly presents variations in spatial statistics. The program allows the type and percentile standard of spatial statistics to be specified by the user, thus enabling an analysis of the geological structure according to variations in spatial statistics. The geotechnical defense-training algorithm shows that a large, linear geological lineament is best visualized using a $3{\times}3$ moving window and a 10% upper standard based on the moving variance value and fractile. In particular, setting the fractile criterion to the upper 0.5% almost entirely eliminates the error values from the contour image.