• Journal for History of Mathematics
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• v.17 no.2
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• pp.15-20
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• 2004

We investigate the unbiasedness and consistency as the statistical properties of density estimators. We show histogram, kernel density estimation, and local adaptive smoothing as density smoothing in this paper. Also, the early and recent research on nonparametric density estimation is described and discussed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.283-286
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• 2014

X-ray 영상의 경우 검은 배경으로 인해 기존의 히스토그램 평활화를 사용하여 대비비를 향상 시킬 경우 문제가 발생한다. 전역 히스토그램 평활화의 경우 영상의 특징을 고려하지 않은 채 전체적으로 히스토그램 평활화가 이루어지기 때문에 부분적인 명암값을 개선시키기 어렵다. BBHE(Bright Preserving Bi-Histogram Equalization)과 DSIHE(Dualistic Sub-Image Histogram Equalization)과 같은 영역별 히스토그램 평활화의 경우 X-ray 사진특성상 검은 배경으로 인하여 히스토그램 평활화를 적용해도 원하는 대비비를 얻기 힘들며 부분적으로 왜곡이 발생한다. 이러한 문제를 해결하기 위해 본 논문에서는 영상의 히스토그램을 통해 배경 정보를 파악하여 밝기 영역을 나눈 후 히스토그램 평활화를 진행함으로써 X-ray 사진의 대비비를 효율적으로 향상시킨다.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.333-344
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• 2001

통계적 추론에 사용되는 많은 통계량들은 평균벡터의 평활함수의 형태로 표현이 가능하다. 본 연구에서는 이들 통계량들의 분포함수에 대한 안부점근사법을 제시하였다. 이 방법은 Na(1998)에서 제시된 일반적 통계량의 분포함수에 대한 안부점근사법이 평균벡터의 평활함수모형에 특히 유용하게 사용될 수 있음을 보인 것이다. 이 근사법은 정규근사에 비해 근사의 정도가 뛰어나며, 특히 통계량의 꼬리부분의 확률에 대해서도 정확도가 그대로 유지되는 장점이 있어 정밀한 추론이 요구되는 많은 문제에 효과적으로 사용될 수 있다. 모의 실험에 사용할 평균벡터의 평활함수 모형으로는 스튜던트화 분산을 고려하였다.

• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.257-262
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• 1996

When we are estimate the smoothing parameter in spline regression model, we deal the diagnostic of influence observations as posteriori analysis. When we use Generalized Maximum Likelihood Function as the estimation method of smoothing parameter, we propose the diagnostic measure for influencial observations in the obtained estimate, and we introduce the finding method of the proper smoothing parameter estimate.

• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.29-36
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• 1998

A Simple, closed form expression is derived for a linear smoothing spline by Eubank (1994, 1997). We introduced his estimater and studied how well examples are fitted to this estimator with the values of minimum generalized cross validation.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.363-376
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• 1998

The linear smoothing spline estimator is modified to remove boundary bias effects. The resulting estimator can be calculated efficiently using an O(n) algorithm that is developed for the computation of fitted values and associated smoothing parameter selection criteria. The asymptotic properties of the estimator are studied for the case of a uniform design. In this case the mean squared error properties of boundary corrected linear smoothing splines are seen to be asymptotically competitive with those for standard second order kernel smoothers.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.283-292
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• 2008

Smoothing parameter or bandwidth plays a key role in nonparametric classification based on kernel density estimation. We consider choosing smoothing parameter in nonparametric classification, which optimize the Bayes risk. Hall and Kang (2005) clarified the theoretical properties of smoothing parameter in terms of minimizing Bayes risk and derived the optimal order of it. Bootstrap method was used in their exploring numerical properties. We compare cross-validation and bootstrap method numerically in terms of optimal order of bandwidth. Effects on misclassification rate are also examined. We confirm that bootstrap method is superior to cross-validation in both cases.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.45-59
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• 2011

When using X-12-ARIMA for seasonal adjustment, we usually check whether the series has stable seasonality or not via D8 F-tests, Kruskal-Wallis test, and the spectral diagnostics. In this paper, we develop several smooth tests for seasonality based on a Fourier series to improve the spectral diagnostics of X-12-ARIMA. A simulation study is conducted to compare five smooth tests for seasonality and X-12-ARIMA's D8 F-test an Kruskal-Wallis test. The simulation study shows that smooth tests for seasonality performed well compared with D8 F-tests and a Kruskal-Wallis test.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.547-560
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• 2008

It is well known that spectral density function determines auto-covariance function of stationary time-series data and smoothed periodogram is a consistent estimator of spectral density function. Recently, Kim and Park (2007) showed that smoothed- periodogram based distances performs very well for the classification. In this paper, we introduce classification methods with smoothed periodogram and apply the approaches to the monthly precipitation measurements obtained from January, 1987 through December, 2007 at 22 locations in South Korea.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.401-407
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• 2012

This paper analyzes performance of major obstacle avoidance methods. For the analysis, numerical performance indexes are proposed: motion distance to goal point, motion time, distance to obstacles, and smoothness of the motion. Especially, the index of smoothness measures efficiency of the motion using the angular acceleration and jerk of the robot heading. Four major obstacle avoidance methods are compared in terms of the performance indexes. The four methods are artificial potential field(APF) method, elastic force(EF) method, APF with virtual distance, and EF with virtual distance. Through simulation, the four methods are compared and features of the methods are explored.