• 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.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.122-136
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• 1995

The objective of this research is to investigate the problem of goodness-of-fit testing based on nonparametric density estimation with a data-driven smoothing parameter. The small and large sample properties of a new test statistic $\hat{\lambda_a}$ is investigated. The test statistic $\hat{\lambda_a}$ is itself a smoothing parameter which is selected to minimize an estimated MISE for a truncated series estimator of the comparison density function. Therefore, this test statistic leads immediately to a point estimate of the density function th the event that $H_0$ is rejected. The limiting distribution of $\hat{\lambda_a}$ is obtained under the null hypothesis. It is also shown that this test is consistent against fixed alternatives.

• 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.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.10a
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• pp.159-162
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• 2003

The remeshing algorithm using the constrained Delaunay method adapted to the mesh density map is developed. In the finite element simulation of forging process, the numerical error increases as the process goes on. However, it is not desirable to use a uniformly fine mesh in the whole domain. Therefore, it is necessary to reduce the analysis error by constructing locally fine mesh at the region where the error is concentrated such as die corner. In this paper, the point insertion algorithm is used and mesh size is controlled by using a mesh density map constructed with a posteriori error estimation. And an optimized smoothing technique is adapted to have smooth distribution and improve the quality of the mesh.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.231-242
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• 1995

In this paper we consider kernel type estimator of the spectral density at a point in the analysis of stationary time series data. The kernel entails choice of smoothing parameter called bandwidth. A data-based bandwidth choice is proposed, and it is obtained by solving an equation similar to Sheather(1986) which relates to the probability density estimation. A Monte Carlo study is done. It reveals that the spectral density estimates using the data-based bandwidths show comparatively good performance.

• 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.

• Transactions of Materials Processing
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• v.13 no.4
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• pp.334-341
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• 2004

In this study, a remeshing algorithm adapted to the mesh density map using the Delaunay mesh generation method is developed. In the finite element simulation of forging process, the numerical error increases as the process goes on because of discrete property of the finite elements and distortion of elements. Especially, in the region where stresses and strains are concentrated, the numerical error will be highly increased. However, it is not desirable to use a uniformly fine mesh in the whole domain. Therefore, it is necessary to reduce the analysis error by constructing locally refined mesh at the region where the error is concentrated such as at the die corner. In this paper, the point insertion algorithm is used and the mesh size is controlled by using a mesh density map constructed with a posteriori error estimation. An optimized smoothing technique is adopted to have smooth distribution of the mesh and improve the mesh element quality.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.137-146
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• 1993

The Proposed goodness-of-fit test Statistic $\hat{\lambda}_{\alpha}$ derived from the test Statistc in Kim (1992) is itself a smoothing parameter which is selected to minimize an estimated MISE for a truncated series estimator, $d_{\hat{\lambda}{n}}$, of the comparison density function. Therefore, this test statistic leads immediately to a point estimate of the density function in the event that $H_{0}$ is ejected. The limiting distribution of $\hat{\lambda}_{\alpha}$ was obtained under the null hypothesis. It is also shown that this test is consistent against fixed alternatives.

• 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.