• Communications for Statistical Applications and Methods
• /
• v.29 no.3
• /
• pp.319-331
• /
• 2022

In this paper, we develop a new statistical model to forecast the PM_2.5 level in Seoul, South Korea. The proposed model is based on the extreme quantile regression model with lasso penalty. Various meteorological variables and air pollution variables are considered as predictors in the regression model, and the lasso quantile regression performs variable selection and solves the multicollinearity problem. The final prediction model is obtained by combining various extreme lasso quantile regression estimators and we construct a binary classifier based on the model. Prediction performance is evaluated through the statistical measures of the performance of a binary classification test. We observe that the proposed method works better compared to the other classification methods, and predicts 'very bad' cases of the PM_2.5 level well.

• Communications for Statistical Applications and Methods
• /
• v.30 no.6
• /
• pp.531-550
• /
• 2023

The estimation of extreme quantiles is one of the main objectives of statistics of extremes (which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error compared to the existing extreme quantile estimators. Practical application of the proposed estimator is illustrated with data from the pedochemical and insurance industries.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.54 no.6
• /
• pp.133-142
• /
• 2012

The objective of this study was to correct the bias of the Representative Concentration Pathways (RCP)-based future precipitation data using a quantile mapping method. This method was adopted to correct extreme values because it was designed to adjust simulated data using probability distribution function. The Generalized Extreme Value (GEV) distribution was used to fit distribution for precipitation data obtained from the Korea Meteorological Administration (KMA). The resolutions of precipitation data was 12.5 km in space and 3-hour in time. As the results of bias correction over the past 30 years (1976~2005), the annual precipitation was increased 16.3 % overall. And the results for 90 years (divided into 2011~2040, 2041~2070, 2071~2100) were that the future annual precipitation were increased 8.8 %, 9.6 %, 11.3 % respectively. It also had stronger correction effects on high value than low value. It was concluded that a quantile mapping appeared a good method of correcting extreme value.

• Atmosphere
• /
• v.25 no.2
• /
• pp.205-219
• /
• 2015

In this study, the simulation results of temperature by regional climate model (Reg- CM4) over South Korea were corrected by Hempel et al. (2013)'s method (Hempel method), and evaluated with the observation data of 50 stations from Korea Meteorological Administration. Among the 30 years (1981~2010) of simulation data, 20 years (1981~2000) of simulation data were used as a training data, and the remnant 10 years (2001~2010) data were used for the evaluation of correction. In general, the Hempel method and parametric quantile mapping show a reasonable correction both in mean and extreme climate of temperature. As the results, the systematic underestimation of mean temperature was greatly reduced after bias correction by Hempel method. And the overestimation of extreme climate, such as the number of TN5% and freezing day, was significantly recovered. In addition to that, the Hempel method better preserved the temporal trend of simulated temperature than other bias correction methods, such as the quantile mapping. However, the overcorrection of the extreme climate related to the upper quantile, such as TX5% and hot days, resulted in the exaggeration of the simulation errors. In general, the Hempel method can reduce the systematic biases embedded in the simulation results preserving the temporal trend but it tends to overcorrect the non-linear biases, in particular, extreme climate related to the upper percentile.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.4
• /
• pp.1441-1448
• /
• 2008

We propose the modified quantile-quantile (Q-Q) plot using the approximate maximum likelihood estimators and the modified normalized sample Lorenz curve (NSLC) plot for the extreme value distribution based on multiply Type-II censored samples. Using two example data sets, we picture the modified Q-Q plot and the modified NSLC plot.

• Journal of the Korean earth science society
• /
• v.44 no.4
• /
• pp.255-263
• /
• 2023

Western coastal area of Chungnam, including Cheonsu Bay and Garorim Bay, has suffered from hot and cold extremes. In this study, the extreme sea surface temperature on the western coast of Chungnam was analyzed using the quantile regression method, which extracts the linear regression values in all quantiles. The regional MOHID (MOdelo HIDrodinâmico) model, with a high resolution on a 1/60° grid, was constructed to reproduce the extreme sea surface temperature. For future prediction, the SSP5-8.5 scenario data of the CMIP6 model were used to simulate sea surface temperature variability. Results showed that the extreme sea surface temperature of Cheonsu Bay in August 2017 was successfully simulated, and this extreme sea surface temperature had a significant negative correlation with the Pacific decadal variability index. As a result of future climate prediction, it was found that an average of 2.9℃ increased during the simulation period of 86 years in the Chungnam west coast and there was a seasonal difference (3.2℃ in summer, 2.4℃ in winter). These seasonal differences indicate an increase in the annual temperature range, suggesting that extreme events may occur more frequently in the future.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.228-232
• /
• 2017

Extreme hydrological events can cause serious threats to the society. Hence, the selection of probability distributions for extreme rainfall is a fundamental issue. For this reason, this study was focused on understanding possible distributional changes in annual daily maximum rainfalls (AMRs) over time in Sri Lanka using quantile regression. A simplified nine-category distributional-change scheme based on comparing empirical probability density function of two years (i.e. the first year and the last year), was used to determine the distributional changes in AMRs. Daily rainfall series of 13 station over Sri Lanka were analyzed for the period of 1960-2015. 4 distributional change categories were identified for the AMRs. 5 stations showed an upward trend in all the quantiles (i.e. 9 quantiles: from 0.05 to 0.95 with an increment of 0.01 for the AMR) which could give high probability of extreme rainfall. On the other hand, 8 stations showed a downward trend in all the quantiles which could lead to high probability of the low rainfall. Further, we identified a considerable spatial diversity in distributional changes of AMRs over Sri Lanka.

• Journal of Climate Change Research
• /
• v.9 no.2
• /
• pp.157-169
• /
• 2018

This study explores the long?term trends of extreme temperatures of 270 observation stations in East Asia (China, Japan, and Korea) for 1961?2013. The 5th percentile of daily minimum temperatures (TN05%) and 95th percentile of daily maximum temperatures (TX95%), derived from the quantile regression, are particularly examined in term of their linear and nonlinear trends. The warming trends of TN05% are typically stronger than those of TX95% with more significant trends in winter than in summer for most stations. In both seasons, warming trends of TN05% tend to amplify with latitudes. The nonlinear trends, quantified by the $2^{nd}$?order polynomial fitting, exhibit different structures with seasons. While summer TN05% and TX95% were accelerated in time, winter TN05% underwent weakening of warming since the 2000s. These results suggest that extreme temperature trends in East Asia are not homogeneous in time and space.

• Communications for Statistical Applications and Methods
• /
• v.30 no.3
• /
• pp.259-272
• /
• 2023

In this paper, we suggest a new method for the prediction of sharp changes in particulate matter (PM₁₀) using quantile mapping. To predict the current PM₁₀ density in Seoul, we consider PM₁₀ and precipitation in Baengnyeong and Ganghwa monitoring stations observed a few hours before. For the PM₁₀ distribution estimation, we use the extreme value mixture model, which is a combination of conventional probability distributions and the generalized Pareto distribution. Furthermore, we also consider a quantile generalized additive model (QGAM) for the relationship modeling between precipitation and PM₁₀. To prove the validity of our proposed model, we conducted a simulation study and showed that the proposed method gives lower mean absolute differences. Real data analysis shows that the proposed method could give a more accurate prediction when there are sharp changes in PM₁₀ in Seoul.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.23 no.2
• /
• pp.63-75
• /
• 2018

This study analyzed the long-term linear trends of the sea surface height around the Korea marginal seas for the period of 1993~2016 by using quantile regression. We found significant difference about 2~3 mm/year for the linear trend between OLS (ordinary least square) and median (50%) quantile regression especially in the Yellow Sea, which is affected by extreme events. Each area shows different trend for each quantile (lower (1%), median (50%) and upper (99%)). Most areas of the Yellow Sea show increasing trend in both low and upper quantile, but significant "upward divergence tendency". This implies that significant increasing trend of upper quantile is higher than that of lower quantile in this area. Meanwhile, South Sea of Korea generally shows "upward convergence tendency" representing that increasing trend of upper quantile is lower than that of lower quantile. This study also confirmed that these tendencies can be eliminated by removing major tidal components from the harmonic analysis. Therefore, it is assumed that the regional characteristics are related to the long term change of tide amplitude.