• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.91-100
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• 1998

A method of estimating probability density using regression tools is presented here. It is based on equal-length binning and locally weighted approximate likelihood for bin counts. The method is particularly useful for densities with bounded supports, where it automatically corrects edge effects without using boundary kernels.

• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.117-126
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• 2015

Forest statistics of local administrative districts have many social needs, nevertheless we have some difficulties for working out an accurate statistics because of insufficient data in small-area level. Thus, new small-area estimation method has to set aside additional data, decrease errors of statistics and consider the local forest characteristics at the same time. In this study, we researched the spatial divisions that can set aside additional data for statistics production and satisfy the major premise, which is "forest characteristics of spatial divisions have to be equal to that of small-area". And we compared synthetic estimation methods based on three different spatial divisions(provinces, neighbor districts and new expanded districts). New expanded districts were divided based on the criteria of climate, soil type and tree species composition that affects local forest characteristics. Small-area statistics were assessed in terms of the ability to estimate local forest characteristics and consistency within large-area statistics. As a result, new expanded districts synthetic estimation was assessed to calculate statistics that reflects local forest characteristics better than other two estimation methods. Moreover, this synthetic estimation method produced the statistics that was included within 95% confidence interval of large-area statistics and was the closer to large-area statistics than the neighbor districts synthetic estimation.

• Journal of the Korean Statistical Society
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• v.33 no.2
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• pp.233-244
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• 2004

The influence of observations is investigated in fitting proportional covariance matrices model. Local influence measures are obtained when all parameters or subsets of the parameters are of interest. We will also derive the local influence measure for investigating the influence of observations in testing the proportionality of covariance matrices. A numerical example is given for illustration.

• Journal of the Korean Statistical Society
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• v.31 no.4
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• pp.447-458
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• 2002

The influence of observations in the equicorrelation model is investigated using the local influence approach when all parameters or subsets of parameters are of interest. When a parameter of interest is scalar, an analytical form of the local influence measure can be found. We will derive a measure for identifying observations that have a large influence on the test of fitting the equicorrelation model. An example is given for illustration.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.465-474
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• 2015

In this paper, we study the local influence analysis of LIU type estimator in the linear mixed models. Using the method proposed by Shi (1997), the local influence of LIU type estimator in three disturbance models are investigated respectively. Furthermore, we give the generalized Cook's distance to assess the influence, and illustrate the efficiency of the proposed method by example.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.78-85
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• 2007

This paper presents a segmental scheme for regions-composed images in local normalization process. The scheme is based on local statistics computed through a moving window. The normalization algorithm uses linear or nonlinear functions to transfer the pixel distribution and the homogeneous affine of regions which is corrupted by additive noise. It adjusts the mean and standard deviation for nearest-neighbor interpoint distance between current and the normalized image signals and changes the segmentation performance according to local statistics and parameter variation adaptively. The performance of newly advanced local normalization algorithm is evaluated and compared to the performance of conventional normalization methods. Experimental results are presented to show the region segmentation properties of these approaches.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.583-594
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• 2021

In this paper, we propose wavelet-based procedures to identify the difference between images, including portraits and handwriting. The proposed methods are based on a novel combination of multiscale methods with a regularization technique. The multiscale method extracts the local characteristics of an image, and the distinct features are obtained through the regularized regression of the local characteristics. The regularized regression approach copes with the high-dimensional problem to build the relation between the local characteristics. Lytle and Yang (2006) introduced the detection method of forged handwriting via wavelets and summary statistics. We expand the scope of their method to the general image and significantly improve the results. We demonstrate the promising empirical evidence of the proposed method through various experiments.

• Journal of the Korean Statistical Society
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• v.13 no.2
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• pp.81-86
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• 1984

Under the i.i.d. hypothesis, authors (1982, 1984) proved some local limit theorems both for the continuous case and for the lattice case. In this paper, results are extended to the case where the random vectors are not identically distributed.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.266-276
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• 1995

We have considered the study of local influence for smoothing parameter estimates in nonparametric regression model. Practically, generalized cross validation(GCV) does not work well in the presence of data perturbation. Thus we have proposed local influence measures for GCV estimates and examined effects of diagnostic by above measures.

• Journal of the Korean Statistical Society
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• v.13 no.1
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• pp.20-24
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• 1984

In analogy to the theorem proved by So and Jeon (1982), we give a multi-dimensional version of local limit theorem for large deviations in the continuous case. We also prove a similar theorem in the case of lattice random vectors. Some examples are given.