• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.643-653
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• 2021

Few studies are found in literature on estimation of population quantiles using the method of ranked set sampling (RSS). The optimal RSS strategy is to select observations with at most two fixed rank order statistics from different ranked sets. In this paper, a near optimal unbalanced RSS model for estimating p^th(0 < p < 1) population quantile is proposed. Main advantage of this model is to use each rank order statistics and is distributionfree. The asymptotic relative efficiency (ARE) for balanced RSS, unbalanced optimal and proposed near-optimal methods are computed for different values of p. We also compared these AREs with respect to simple random sampling. The results show that proposed unbalanced RSS performs uniformly better than balanced RSS for all set sizes and is very close to the optimal RSS for large set sizes. For the practical utility, the near optimal unbalanced RSS is recommended for estimating the quantiles.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.211-222
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• 2013

We consider the problem of nonparametrically estimating the conditional quantile function from censored data and propose new estimators here. They are based on local logistic regression technique of Lee et al. (2006) and "double-kernel" technique of Yu and Jones (1998) respectively, which are modified versions under random censoring. We compare those with two existing estimators based on a local linear fits using the check function approach. The comparison is done by a simulation study.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2850-2858
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• 2024

Amidst the uncertainties of climate policy, investing in nuclear energy technology emerges as a sustainable strategy, fostering innovation in a critical sector, while simultaneously addressing urgent environmental concerns and managing budgetary dynamics. Our investigation inspects the asymmetric influence of climate policy uncertainty on nuclear energy technology in the top 10 nations with the highest nuclear energy R&D budgets (Germany, Japan, China, France, USA, UK, India, South Korea, Russia, and Canada). Previous studies adopted panel data methods to evaluate the linkage between climate policy uncertainty and nuclear energy technology. Nonetheless, these investigations overlooked the variability in this association across various countries. Conversely, this investigation introduces an innovative tool, 'Quantile-on-Quantile' to probe this connection merely for every economy. This methodology concedes for a more accurate evaluation, offering a holistic global perspective and delivering tailored insights for individual countries. The findings uncover that climate policy uncertainty significantly reduces nuclear energy technology budgets across multiple quantiles in most selected economies. Additionally, our results highlight the asymmetries in the correlations between our variables across the nations. These findings stress the need for policymakers to conduct thorough assessments and skillfully manage climate policy uncertainty and nuclear energy budgets.

• KDI Journal of Economic Policy
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• v.32 no.3
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• pp.71-99
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• 2010

The concept of CoVaR introduced by Adrian and Brunnermeier (2009) is a useful tool to measure the risk spillover effect. It can capture the risk contribution of each institution to overall systemic risk. While Adrian and Brunnermeier rely on the quantile regression method in the estimation of CoVaR, we propose a new estimation method using parametric distribution functions such as bivariate normal and $S_U$-normal distribution functions. Based on our estimates of CoVaR for Korean banking industry, we investigate the practical usefulness of CoVaR for a systemic risk measure, and compare the estimation performance of each model. Empirical results show that bank makes a positive contribution to system risk. We also find that quantile regression and normal distribution models tend to considerably underestimate the CoVaR (in absolute value) compared to $S_U$-normal distribution model, and this underestimation becomes serious when the crisis in a financial system is assumed.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.827-837
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• 2012

The estimation of the rainfall quantile is of great importance in designing hydrologic structures. Conventionally, the rainfall quantile is estimated by univariate frequency analysis with an appropriate probability distribution. There is a limitation in which duration of rainfall is restrictive. To overcome this limitation, bivariate frequency analysis by using 3 copula models is performed in this study. Annual maximum rainfall events in 5 stations are used for frequency analysis and rainfall depth and duration are used as random variables. Gumbel (GUM), generalized logistic (GLO) distributions are applied for rainfall depth and generalized extreme value (GEV), GUM, GLO distributions are applied for rainfall duration. Copula models used in this study are Frank, Joe, and Gumbel-Hougaard models. Maximum pseudo-likelihood estimation method is used to estimate the parameter of copula, and the method of probability weighted moments is used to estimate the parameters of marginal distributions. Rainfall quantile from this procedure is compared with various marginal distributions and copula models. As a result, in change of marginal distribution, distribution of duration does not significantly affect on rainfall quantile. There are slight differences depending on the distribution of rainfall depth. In the case which the marginal distribution of rainfall depth is GUM, there is more significantly increasing along the return period than GLO. Comparing with rainfall quantiles from each copula model, Joe and Gumbel-Hougaard models show similar trend while Frank model shows rapidly increasing trend with increment of return period.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.561-576
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• 2007

Five plotting positions are applied to the computation of probability weighted moments (PWM) on the parameters of the generalized logistic distribution. Over a range of parameter values with some finite sample sizes, the effects of five plotting positions are investigated via Monte Carlo simulation studies. Our simulation results indicate that the Landwehr plotting position frequently tends to document smaller biases than others in the location and scale parameter estimations. On the other hand, the Weibull plotting position often tends to cause larger biases than others. The plotting position (i - 0.35)/n seems to report smaller root mean square errors (RMSE) than other plotting positions in the negative shape parameter estimation under small samples. In comparison to the maximum likelihood (ML) method under the small sample, the PWM do not seem to be better than the ML estimators in the location and scale parameter estimations documenting larger RMSE. However, the PWM outperform the ML estimators in the shape parameter estimation when its magnitude is near zero. Sensitivity of right tail quantile estimation regarding five plotting positions is also examined, but superiority or inferiority of any plotting position is not observed.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.15-27
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• 2001

One proposal is made for constructing nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of idea of Johns for estimating the center of the symmetric distribution together with the idea of regression quantiles and regression trimmed mean. This nonparametric estimator and some other L-estimators are studied by Monte Carlo.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.325-335
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• 2002

In this paper we introduce some adaptive M-estimators using selector statistics to estimate the center of symmetric and continuous underlying distributions. This selector statistics is based on the idea of Hogg(1983) and Hogg et. al. (1988) who used averages of some order statistics to discriminate underlying distributions. In this paper, we use the functions of sample quantiles as selector statistics and determine the suitable quantile points based on maximizing the distance index to discriminate distributions under consideration. In Monte Carlo study, this robust estimation method works pretty good in wide range of underlying distributions.

• Journal of Korea Water Resources Association
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• v.50 no.1
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• pp.1-15
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• 2017

Information on radar rainfall with high spatio-temporal resolution over large areas has been used to mitigate climate-related disasters such as flash floods. On the other hand, a well-known problem associated with the radar rainfall using the Marshall-Palmer relationship is the underestimation. In this study, we develop a new bias correction scheme based on the quantile regression method. This study employed a bivariate copula function method for the joint simulation between radar and ground gauge rainfall data to better characterize the error distribution. The proposed quantile regression based bias corrected rainfall showed a good agreement with that of observed. Moreover, the results of our case studies suggest that the copula function approach was useful to functionalize the error distribution of radar rainfall in an effective way.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.657-669
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• 2019

To estimate accurate design quantiles considering statistical characteristics of hydrological data is one of the most important procedures in the design of hydraulic structures. While at-site frequency analysis estimates design quantile using observed data at a site of interest, regional frequency analysis (RFA) utilizes a number of sites included in a hydrologically homogeneous region. Therefore, RFA could provide a more accurate design quantile at ungauged site or sites with short observation period. In this review article, RFA is classified into stationary RFA and nonstationary RFA depending on the characteristic of hydrological data, and the basic concept, procedure, and application of each technique are explained in detail focused on the index flood method. Additionally, a review of the state of the art for RFA procedure is presented. This paper is finalized by describing the stationary regional rainfall frequency analysis over South Korea contained in the amendment of "Standard guidelines for design flood estimation" and various future study topics related to nonstationary RFA.