• Environmental and Resource Economics Review
• /
• v.13 no.1
• /
• pp.83-117
• /
• 2004

This study investigates the spatial distributions of the ambient levels of air pollutants ($SO_2$, $NO_2$, $O_3$, CO, and PM) in Seoul metropolitan area using the data obtained by the air pollution observation stations. This study estimated a non-parametric kernel density function and two types of inequality indices, Gini and Entropy. Our estimation results show that the degree of inequality in spatial distribution of air pollution, in general, tends to be stable or slightly decreasing for the period of 1990~2001. In addition, we found that there are significant dynamics of air pollution levels in terms of spatial ranking.

• Communications for Statistical Applications and Methods
• /
• v.18 no.4
• /
• pp.457-465
• /
• 2011

Sample entropy (Vasicek, 1976) has poor performance, and several nonparametric entropy estimators have been proposed as alternatives. In this paper, we consider a piecewise uniform density function based on quantiles, which enables us to evaluate entropy in each interval, and study the poor performance of the sample entropy in terms of the poor estimation of lower and upper quantiles. Then we propose some improved entropy estimators by simply modifying the quantile estimators, and compare their performances with some existing estimators.

• The Pure and Applied Mathematics
• /
• v.1 no.1
• /
• pp.19-24
• /
• 1994

Let P be a probability measure on the real line with Lebesque-density f. The usual estimator of the distribution function (≡df) of P for the sample $\chi$$_1$,…, $\chi$$\_$n/ is the empirical df: F$\_$n/(t)=(equation omitted). But this estimator does not take into account the smoothness of F, that is, the existence of a density f. Therefore, one should expect that an estimator which is better adapted to this situation beats the empirical df with respect to a reasonable measure of performance.(omitted)

• Structural Engineering and Mechanics
• /
• v.75 no.6
• /
• pp.771-784
• /
• 2020

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.30 no.1
• /
• pp.87-94
• /
• 2017

Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.2B
• /
• pp.87-98
• /
• 2011

Recently, occurrence frequency of extreme events like flood and drought is increasing due to climate change by global warming. Especially, a drought is more severer than other hydrologic disasters because it causes continuous damage through long period. But, ironically, it is difficult to recognize the importance and seriousness of droughts because droughts occur for a long stretch of time unlike flood. So as to analyze occurrence of droughts and prepare a countermeasure, this study analyzed a meteorologic drought among many kinds of drought that it is closely related with precipitation. Palmer Drought Severity Index, Standard Precipitation and Effective Drought Index are computed using precipitation and temperature material observed by Korean Meteorological Administration. With the result of comparative analysis of computed drought indices, Effective Drought Index is selected to execute frequency analysis because it is accordant to past droughts and has advantage to compute daily indices. A Frequency analysis of Effective Drought Index was executed using boundary kernel density function. In the result of analysis, occurrence periods of spring showed about between 10 year and 20 year, it implies that droughts of spring are more frequent than other seasons. And severity and occurrence period of droughts varied in different regions as occurrence periods of the Youngnam region and the southern coast of Korea are relatively shorter than other regions.

• The Korean Journal of Applied Statistics
• /
• v.26 no.1
• /
• pp.141-149
• /
• 2013

We present methods for studying the log-density ratio that enables the selection of the predictors and the form to be included in the logistic regression model. Under bivariate normal distributional assumptions, we investigate the form of the log-density ratio as a function of two predictors. If two covariance matrices are equal, then the crossproduct and quadratic terms are not needed. If the variables are uncorrelated, we do not need the crossproduct terms, but we still need the linear and quadratic terms. We also explore other conditions in which the crossproduct and quadratic terms are not needed in the logistic regression model.

• Land and Housing Review
• /
• v.14 no.2
• /
• pp.35-50
• /
• 2023

The purpose of this study is to identify areas in need of urban renewal by utilizing spatial data and analyzing their types and characteristics. For this, this research employed a kernel density function and K-means cluster analysis with spatial data, through which it sought ways to identify high-demand areas for urban renewal projects. The key findings and implications of the research are summarized as follows. Firstly, this research classified 587 target sites in Seoul based on development density (ratios) and an indicator for aged buildings. Approximately half of these areas were consistent with leading pilot project sites and Accelerated Integration Sites. Secondly, it was observed that residential environments in the designated leading pilot project sites, as decided by public sectors, were relatively poor compared to other areas. Lastly, the target areas for urban renewal were not clearly categorized through statistical analysis. Instead, it was found that categorization should be made depending on the requirements of each project.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2011.05a
• /
• pp.32-36
• /
• 2011

가뭄은 홍수와 같이 단기간에 피해를 발생시키는 것이 아니라 장기간에 걸쳐 서서히 진행되므로 그 심각성을 인식하기 어렵고 국가 차원의 대책 또한 미비한 실정이다. 따라서 본 연구에서는 가뭄의 발생특성을 파악하기 위해 기상학적 가뭄지수를 산정하여 가뭄빈도해석을 실시하였다. 빈도해석방법은 weibull분포를 이용한 매개변수적 방법과 경계핵밀도함수(Boundary Kernel Density Function)를 이용한 비매개변수적 방법을 병행하여 재현기간별 가뭄심도를 산정하였다.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.1
• /
• pp.531-536
• /
• 2022

This paper compared the performance of the parametric method and the nonparametric method when estimating the distribution for the tail of the distribution with heavy tails. For the parametric method, the generalized extreme value distribution and the generalized Pareto distribution were used, and for the nonparametric method, the kernel density estimation method was applied. For comparison of the two approaches, the results of function estimation by applying the block maximum value model and the threshold excess model using daily fine dust public data for each observatory in Seoul from 2014 to 2018 are shown together. In addition, the area where high concentrations of fine dust will occur was predicted through the return level.