• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.507-514
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• 1998

The minimum distance estimation based on the L$_2$ distance between a model density and a density estimator is studied from M-estimation point of view. We will show that how a model density and a density estimator are incorporated in order to create an M-estimation function. This method enables us to create an M-estimating function reflecting the natures of both an assumed model density and a given set of data. Some new types of M-estimation functions for estimating a location and scale parameters are introduced.

• 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.

• Journal of the Korean Wood Science and Technology
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• v.49 no.6
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• pp.541-549
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• 2021

This paper reports a method to evaluate the residual strength of insect-damaged radiata pine lumber, such as the screw withdrawal strength as a semi-destructive method and a compression parallel to the grain test to assess the density changes after exposure to outdoor conditions. The screw withdrawal strength test was used as a semi-destructive method to estimate the residual density of decayed lumber. A compression parallel to the grain test was applied to evaluate the residual density. Three variables, such as the screw withdrawal strength, compression parallel to the grain, and residual density, were analyzed statistically to evaluate their relationships. The relationship between the residual density and screw withdrawal strength showed a good correlation, in which the screw withdrawal strength decreased with decreasing density. The other relationship between the residual density and compression parallel to the grain was also positively correlated; the compression parallel to the grain strength decreased with decreasing density. Finally, the correlation between the three variables was statistically significant, and the mutual correlation coefficients showed a strong correlation between the three variables. Hence, these variables are closely correlated. The test results showed that the screw withdrawal strength could be used as a semi-destructive method for an in situ estimation of an existing wood structure. Moreover, the method might approximate the residual density and compression parallel to the grain if supplemented with additional data.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.939-946
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• 2008

In this paper the support vector method is presented for the probability density function estimation when the sample observations are contaminated with random noise. The performance of the procedure is compared to kernel density estimates by the simulation study.

• 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 Korea Society of Computer and Information
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• v.22 no.11
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• pp.49-55
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• 2017

Recently, for safety of people, there are proposed so many technologies which detect density of people at the specific place or space. The representative technology for crowd density estimation was using image analysis method from CCTV images. However, this method had a weakness which could not be used and which's accuracy was lower at the dark or smog space. Therefore, in this paper, to solve this problem, we proposed a user density estimation system at closed space using high frequency and smart device. The system send inaudible high frequencies to smart devices and it count the smart devices which detect the high frequencies on the space. We tested real-time user density with the proposed system and ten smart devices to evaluate performance. According to the testing results, we confirmed that the proposed system's accuracy was 95% and it was very useful. Thus, because the proposed system could estimate about user density at specific space exactly, it could be useful technology for safety of people and measurement of space use state at indoor space.

• 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 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 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.

• Genomics & Informatics
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• v.20 no.2
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• pp.17.1-17.11
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• 2022

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.