• Journal of the Korean Statistical Society
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• v.27 no.2
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• pp.153-163
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• 1998

We investigate density estimation problem in the L$^{\infty}$ norm and show that the iii optimal minimax rates are achieved for smooth classes of weakly dependent stationary sequences. Our results are then applied to give uniform convergence rates for various problems including the Gibbs sampler.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2520-2527
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• 2000

An improved transition density propagation method for power estimation is proposed. The power estimation for the zero delay model is a proper criteria for the.lower boutldlIry for power consumption. A transition propagation method, including the zero delay model as a lower boundary for power stimation was studied. However, there were some redundancy factors in the process of transition density propagation. Hence this paper will explore the transition density propagation behavior to eliminate the redundancy factors and present theirriprQved estimation methodology for the signal transition density. The experiments show that the proposed method has comparably better estimation accuracy than the conventional methods.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.1-49
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• 2001

The skin color model is a very important concept in face detection, face recognition and face tracking. Usually, this model is obtained by estimating a probability density function of skin color distribution. In many cases, it is assumed that the underlying density function follows a Gaussian distribution. In this paper, a new method for non-parametric estimation of the probability density function, by using feed-forward neural network, is used to estimate the underlying skin color model. By using this method, the resulting skin color model is better than the Gaussian estimation and substantially approaches the real distribution. Applications to face detection and face ...

• Journal of the Korean Wood Science and Technology
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• v.23 no.3
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• pp.58-65
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• 1995

The possibilities of the estimation of the moisture content(MC) for sitka-spruce (Picea sitchensis Carr.) by measuring density have been investigated. The method is based on the relationships between the wood density and moisture content of wood expressed by Equations (8)~(9). The purpose of this study is examining the estimation of the moisture content of wood by density and the variation of moisture content with annual ring width of wood. The following conclusions were obtained; 1. This method is very convenience because of the average moisture content of wood can be obtained by a simple estimation. This estimation can be made from the easy measurement of the weight and volume of wood. 2. Coefficient of determination between the experimental MCs and theoretical MCs which is calculated by the oven-dry densities of each specimens and Equations (8), (9) is 0.98. This Correlation is very remarkable. Therefore the model Equations on the estimation of moisture content by wood density was available. 3. Relationship between experimental MCs and theoretical MCs which is estimated by average oven-dry density of total specimens showed positive correlation(Fig.2). But from the Fig.4. we can concluded that the number of specimens is two groups. This phenomenon is considered that the variation of MC by the annual ring width from the specimens' observations. Consequently, the MCs of wood by density, is likely to be successful method. can be estimate using by the average oven-dry densities divided with the annual ring widths of wood.

• Journal for History of Mathematics
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• v.16 no.3
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• pp.95-100
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• 2003

The recent surge of interest in the more technical aspects of nonparametric density estimation and nonparametric regression estimation has brought the subject into public view. In this paper, we investigate the general concept of the nonparametric density estimation, the nonparametric regression estimation and its performance criteria.

• Journal of the Korean earth science society
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• v.26 no.2
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• pp.114-128
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• 2005

Fuzzy information representation of multi-source spatial data is applied to landslide hazard mapping. Information representation based on frequency ratio and non-parametric density estimation is used to construct fuzzy membership functions. Of particular interest is the representation of continuous data for preventing loss of information. The non-parametric density estimation method applied here is a Parzen window estimation that can directly use continuous data without any categorization procedure. The effect of the new continuous data representation method on the final integrated result is evaluated by a validation procedure. To illustrate the proposed scheme, a case study from Jangheung, Korea for landslide hazard mapping is presented. Analysis of the results indicates that the proposed methodology considerably improves prediction capabilities, as compared with the case in traditional continuous data representation.

• Journal of the Korean Data and Information Science Society
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• v.3 no.1
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• pp.17-24
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• 1992

The problem of estimating symmetric probability density with high kurtosis is considered. Such densities are often estimated poorly by a global bandwidth kernel estimation since good estimation of the peak of the distribution leads to unsatisfactory estimation of the tails and vice versa. In this paper, we propose a transformation technique before using a global bandwidth kernel estimator. Performance of density estimator based on proposed transformation is investigated through simulation study. It is observed that our method offers a substantial improvement for the densities with high kurtosis. However, its performance is a little worse than that of ordinary kernel estimator in the situation where the kurtosis is not high.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.76-78
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• 2014

Naive Bayes (NB, for shortly) learning is more popular, faster and effective supervised learning method to handle the labeled datasets especially in which have some noises, NB learning also has well performance. However, the conditional independent assumption of NB learning imposes some restriction on the property of handling data of real world. Some researchers proposed lots of methods to relax NB assumption, those methods also include attribute weighting, kernel density estimating. In this paper, we propose a novel approach called NB Based on Attribute Weighting in Kernel Density Estimation (NBAWKDE) to improve the NB learning classification ability via combining kernel density estimation and attribute weighting.

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.453-465
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• 2001

It is often the case that one wants to estimate parameters of the distribution which follows certain parametric model, while the dta are contaminated. it is well known that the maximum likelihood estimators are not robust to contamination. Basuet al.(1998) proposed a robust method called the minimum density power divergence estimation. In this paper, we investigate data-driven selection of the tuning parameter $\alpha$ in the minimum density power divergence estimation. A criterion is proposed and its performance is studied through the simulation. The simulation includes three cases of estimation problem.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1159-1165
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• 2005

Basu et al. (1998) proposed a new density-based estimator, called the minimum density power divergence estimator (MDPDE), which avoid the use of nonparametric density estimation and associated complication such as bandwidth selection. Woodward et al. (1995) examined the minimum Hellinger distance estimator (MHDE), proposed by Beran (1977), in the case of estimation of the mixture proportion in the mixture of two normals. In this article, we introduce the MDPDE for a mixture proportion, and show that both the MDPDE and the MHDE have the same asymptotic distribution at a model. Simulation study identifies some cases where the MHDE is consistently better than the MDPDE in terms of bias.