• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.137-141
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• 2009

We study on selecting the kernel weighting function in smoothing moment conditions for dependent processes. For hypothesis testing in Generalized Method of Moments or Generalized Empirical Likelihood context, we find that smoothing moment conditions by Bartlett kernel delivers smallest size distortions based on empirical Edgeworth expansions of the long-run variance estimator.

• Journal of applied mathematics & informatics
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• v.8 no.3
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• pp.731-742
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• 2001

A Gaussian smoothing algorithm obtained from a cascade of convolutions with a seven-point kernel is described. We prove that the change of local sums after applying our algorithm to sinusoidal signals is reduced to about two thirds of the change by the binomial coefficients. Hence, our seven point kernel is better than the binomial coefficients when trend curves are needed to be generated. We also prove that if our Gaussian convolution is applied to sinusoidal functions, the amplitude of higher frequencies reduces faster than the lower frequencies and hence that it is a low pass filter.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.3
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• pp.289-297
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• 2011

The explicit finite element method is an essential tool for solving large problems with severe nonlinear characteristics, but its results can be difficult to interpret. In particular, it can be impossible to evaluate its acceleration responses because of severe discontinuity, extreme noise or aliasing. We suggest a new post-processing method for transient responses and their response spectra. We propose smoothing methods using a Gaussian kernel without in depth knowledge of the complex frequency characteristics; such methods are successfully used in the filtering of digital signals. This smoothing can be done by measuring the velocity results and monitoring the response spectra. Gaussian kernel smoothing gives a better smoothness and representation of the peak values than other approaches do. The floor response spectra can be derived using smoothed accelerations for the design.

• Proceedings of the KSMRM Conference
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• 2003.10a
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• pp.75-75
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• 2003

목적： MR 영상에 나타나는 bias field, 즉 영상의 특정 부분이 주위보다 어둡거나 밝게 나타나는 현상을 보다 균일하게 보정시키는 방법으로 제시된 N3 방법에서 Gaussian kernel을 사용한 smoothing 방법 대신에 Wavelet(Daubechies, D4)함수를 smoothing기법으로 사용했을 때 어느 정도 균일함에 향상이 일어나는지를 알아보는 것이다.

• Journal for History of Mathematics
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• v.19 no.3
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• pp.113-122
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• 2006

We investigate the mathematical background, statistical methodology, and the teaching of computer-software field using smoothing methodology in this paper. Also we investigate conception and methodology of histogram, kernel density estimator, adaptive kernel estimator, bandwidth selection based on mathematics and statistics.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.515-529
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• 1998

Burman (1987) and Hall and Titterington (1987) studied kernel smoothing for sparse multinomial data in detail. Both of their estimators for cell probabilities are sparse asymptotic consistent under some restrictive conditions on the true cell probabilities. Dong and Simonoff (1994) adopted boundary kernels to relieve the restrictive conditions. We propose a local linear kernel estimator which is popular in nonparametric regression to estimate cell probabilities. No boundary adjustment is necessary for this estimator since it adapts automatically to estimation at the boundaries. It is shown that our estimator attains the optimal rate of convergence in mean sum of squared error under sparseness. Some simulation results and a real data application are presented to see the performance of the estimator.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.457-464
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• 2012

Small area estimation is a statistical inference method to overcome the large variance due to the small sample size allocated in a small area. Recently some nonparametric estimators have been applied to small area estimation. In this study, we suggest a nonparametric mixed effect small area estimator using kernel smoothing and compare the small area estimators using labor statistics.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1049-1058
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• 2012

Nonparametric methods are often used as an alternative to parametric methods to estimate density function and regression function. In this paper we consider improved methods to select the Bezier points in Bezier curve smoothing that is shown to have the same asymptotic properties as the kernel methods. We show that the proposed methods are better than the existing methods through numerical studies.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.227-230
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• 1995

In this paper, we discuss simple ways of implementing non-basic kernel density estimators which typically ceed extra pilot estimation. The methods utilize order statistics at the pilot estimation stages. We focus mainly on bariable lacation and scale kernel density estimator (Jones, Hu and McKay, 1994), but the same idea can be applied to other methods too.