• Journal of Labour Economics
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• v.30 no.2
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• pp.87-118
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• 2007

Using KRIVET's Graduates Economic Activities Survey for 2005, this article examines the relationship between university ranking and labor market outcome, with a focus on wage differentials existing between provincial and Seoul metropolitan area university graduates. According to the analysis results, the average monthly wage for provincial university graduates was 1,747.7 thousand Korean won, which is 11.5% lower than that for graduates of universities in the Seoul metropolitan area. School effects on individual wage were estimated to about 12.2% after applying Hierarchical Linear Model technique, which means that university explains only an insignificant part of the total variance in wage among graduates. After controlling for the selection bias, the ability difference between the two areas, by applying the Heckman type 2SLS wage function and Neumark wage differential decomposition technique, the wage gap resulting from the segregation was not identified. This implies that, to a significant extent, the wage gap between provincial and Seoul metropolitan university graduates is attributed to the difference in productivity among individual graduates, rather than to the wage segregation. Also, the estimated wage function by applying Quantile Regression technique indicates that there does not exist any significant wage segregation difference by wage quantile.

• Journal of IKEEE
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• v.5 no.1 s.8
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• pp.1-7
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• 2001

In this paper, we have investigated the CDMA(Code Division Multiple Access) Cellular System with non-linear equalizer in reverse link channel. In general, due to unknown characteristics of channel in the wireless communication, the distribution of the observables cannot be specified by a finite set of parameters; instead, we partitioned the m-dimensional sample space Into a finite number of disjointed regions by using quantiles and a vector quantizer based on training samples. The algorithm proposed is based on a piecewise approximation to regression function based on quantiles and conditional partition moments which are estimated by Robbins Monro Stochastic Approximation (RMSA) algorithm. The resulting equalizers and detectors are robust in the sense that they are insensitive to variations in noise distributions. The main idea is that the robust equalizers and robust partition detectors yield better performance in equiprobably partitioned subspace of observations than the conventional equalizer in unpartitioned observation space under any condition. And also, we apply this idea to the CDMA system and analyze the BER performance.

• Journal of Korea Water Resources Association
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• v.55 no.9
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• pp.701-705
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• 2022

Reliable drought inflow scenarios are required to plan reservoirs in response to the present severe drought-like conditions. However, the previously developed method for generating drought inflows, the K-water cumulative difference method (KCM), is considered inadequate owing to its potential for negative inflow, reversal phenomena, and overestimation. Nevertheless, the occurrence of these aspects has not been theoretically analyzed. Hence, this study employed the quantile function and frequency factor for log-normal and Gumbel distributions to quantify the contributing factors of these limitations. Consequently, it was found that the negative inflows are generated when the difference in the location parameters, during the accumulation process, exceeds that of the scale parameters. In addition, as the standard deviation decrease during the accumulation process, the reversal phenomena, and inflated values prevailed.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.493-505
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• 2017

Maximum likelihood estimation (MLE) of the generalized extreme value distribution (GEVD) is known to sometimes over-estimate the positive value of the shape parameter for the small sample size. The maximum penalized likelihood estimation (MPLE) with Beta penalty function was proposed by some researchers to overcome this problem. But the determination of the hyperparameters (HP) in Beta penalty function is still an issue. This paper presents some data adaptive methods to select the HP of Beta penalty function in the MPLE framework. The idea is to let the data tell us what HP to use. For given data, the optimal HP is obtained from the minimum distance between the MLE and MPLE. A bootstrap-based method is also proposed. These methods are compared with existing approaches. The performance evaluation experiments for GEVD by Monte Carlo simulation show that the proposed methods work well for bias and mean squared error. The methods are applied to Blackstone river data and Korean heavy rainfall data to show better performance over MLE, the method of L-moments estimator, and existing MPLEs.

• Journal of Korean Society on Water Environment
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• v.31 no.3
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• pp.241-252
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• 2015

Runoff behaviors by five bias correction methods were analyzed, which were Change Factor methods using past observed and estimated data by the estimation scenario with average annual calibration factor (CF_Y) or with average monthly calibration factor (CF_M), Quantile Mapping methods using past observed and estimated data considering cumulative distribution function for entire estimated data period (QM_E) or for dry and rainy season (QM_P), and Integrated method of CF_M+QM_E(CQ). The peak flow by CF_M and QM_P were twice as large as the measured peak flow, it was concluded that QM_P method has large uncertainty in monthly runoff estimation since the maximum precipitation by QM_P provided much difference to the other methods. The CQ method provided the precipitation amount, distribution, and frequency of the smallest differences to the observed data, compared to the other four methods. And the CQ method provided the rainfall-runoff behavior corresponding to the carbon dioxide emission scenario of SRES A1B. Climate change scenario with bias correction still contained uncertainty in accurate climate data generation. Therefore it is required to consider the trend of observed precipitation and the characteristics of bias correction methods so that the generated precipitation can be used properly in water resource management plan establishment.

• Journal of Korean Society of Rural Planning
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• v.28 no.2
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• pp.41-50
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• 2022

The primary purpose of this study is to identify various factors that affect farmland prices according to changes in the actual transaction price of farmland over the past decade, and to use this to derive policy implications for price stabilization. To this end, the farmland price model are constructed at the parcel level in the case area (Namwon-si, Jinju-si). The analysis method is based on the Hedonic price function, and the OLS and the quantile regression are used for the parcel level model. As a result of estimating the parcel level farmland price model in the case area, the larger the parcel area, the lower the farmland price, and the higher the farmland price outside the agricultural promotion area. It was found that there was a price difference according to the type of special purpose areas, and the location characteristics showed some differences across the cities. The farmland price models presented in this study are suitable for identifying the factors affecting farmland prices, and are expected to be highly utilized in that it is possible to construct flexible variables suitable for regional characteristics.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.13-13
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• 2020

'20년 3월 현재 전국 3,502개 읍면동 중 73개 읍면동이 지하수를 상수원으로 급수 중이며, 48개 산업단지에서 지하수를 주 수원으로 사용 중이다. 또한 급수 소외지역의 물 공급을 위해 주로 사용되는 소규모수도시설 14,811개 중 12,073개(81.5%)는 지하수를 이용하고 있으며, 그 위치는 전국에 산재해 있다. 이처럼 지하수는 댐, 저수지 및 하천과 더불어 생·공용수의 중요한 수원이라 할 수 있다. 본 연구에서는 급수 소외지역의 주요 수원인 지하수위 현황을 이용한 가뭄 모니터링 및 전망 기법을 개발하고자 하였다. 국가 지하수관측망 중 10년 이상 장기 관측 자료를 보유한 253개 관측소의 일단위 관측자료를 기반으로, 과거 관측수위 분포를 핵밀도함수로 추정하고 Quantile Function을 이용해 현재 수위의 높고 낮은 정도를 Percentile 값으로 산정하였다. 관측소별 지하수위 Percentile은 티센망을 이용해 167개 시군별로 공간평균하고 Percentile의 범위에 따른 가뭄등급을 설정하여 지하수 가뭄 정도를 모니터링 할 수 있는 기법을 제시하였다. 또한 지하수 가뭄을 전망하기 위해 강수와 지하수위의 거시적인 응답특성을 이용하였다. 관측소별로 추정된 핵밀도함수의 누적확률을 표준정규분포의 Quantile로 변환하여 표준지하수지수I(Standardized Groundwater level Index, SGI)를 산정하고, 시군별로 공간을 일치시킨 1~12개월 지속기간별 표준강수지수(Standardized Precipitation Index, SPI)와의 상관관계를 이용해 NARX(nonlinear autoregressive exogenous) 인공신경망 예측모형을 구축하였다. 이를 통해 기상청 정량전망 강수량을 이용해 전국의 1~3개월 후 지하수 가뭄을 빠르게 전망할 수 있는 체계를 구축하고, 생·공용수 분야 국가 가뭄 예·경보의 미급수지역 가뭄현황 및 전망에 활용중이다.

• Journal of Korea Technology Innovation Society
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• v.21 no.3
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• pp.1077-1100
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• 2018

The present study analyzed the effect of firms' external research and development (R&D) on corporate productivity, while investigating the moderating effect of firm size on the external R&D-productivity nexus. In the empirical analysis, we estimated South Korean manufacturing firms' total factor productivity (TFP) using the firm level data drawn from the Survey of Business Activities (Korea National Statistical Office) for the years 2006-2015. Thereafter, focusing on the role of external R&D and its interaction with the firm size in determining firms' TFP, the productivity function was estimated as well. To this end, we used ordinary least squares (OLS) and quantile regression to highlight the heterogeneous impacts of external R&D by companies' productivity level. Empirical results confirmed that firms' external R&D significantly enhanced corporate productivity in all manufacturing industries, from high-tech to low-tech. The moderating effect of firm size in determining the productivity effect of external R&D was not as prominent as in the case for internal R&D, which exhibited some degree of the size premium in the productivity-enhancing effect. These results suggest that regardless of the firm size, external R&D can be an important channel for corporate productivity improvement, and can be a particularly effective strategy for SMEs with relatively limited internal R&D capacities.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.155-163
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• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.545-552
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• 2003

Let X$_1$, X$_2$, … be real valued random variables under linearly negatively quadrant dependent (LNQD). In this paper, we discuss the probability inequality of ennett(1962) and Hoeffding(1963) under some suitable random variables. These results are to extend Theorem A and B to LNQD random variables. Furthermore, let ζdenote the ｐth quantile of the marginal distribution function of the $Ｘ_i$'s which is estimated by a smooth estima te $ζ_{pn}$, on the basis of X$_1$, X$_2$, …$X_n$. We establish a convergence of $ζ_{pn}$, under Hoeffding-type probability inequality of LNQD.