• Title/Summary/Keyword: kernel density

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Reducing Bias of the Minimum Hellinger Distance Estimator of a Location Parameter

  • Pak, Ro-Jin
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.1
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    • pp.213-220
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    • 2006
  • Since Beran (1977) developed the minimum Hellinger distance estimation, this method has been a popular topic in the field of robust estimation. In the process of defining a distance, a kernel density estimator has been widely used as a density estimator. In this article, however, we show that a combination of a kernel density estimator and an empirical density could result a smaller bias of the minimum Hellinger distance estimator than using just a kernel density estimator for a location parameter.

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Utilizing Order Statistics in Density Estimation

  • Kim, W.C.;Park, B.U.
    • 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.

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Naive Bayes Approach in Kernel Density Estimation (커널 밀도 측정에서의 나이브 베이스 접근 방법)

  • Xiang, Zhongliang;Yu, Xiangru;Al-Absi, Ahmed Abdulhakim;Kang, Dae-Ki
    • 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.

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ASYMPTOTIC APPROXIMATION OF KERNEL-TYPE ESTIMATORS WITH ITS APPLICATION

  • Kim, Sung-Kyun;Kim, Sung-Lai;Jang, Yu-Seon
    • Journal of applied mathematics & informatics
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    • v.15 no.1_2
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    • pp.147-158
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    • 2004
  • Sufficient conditions are given under which a generalized class of kernel-type estimators allows asymptotic approximation on the modulus of continuity. This generalized class includes sample distribution function, kernel-type estimator of density function, and an estimator that may apply to the censored case. In addition, an application is given to asymptotic normality of recursive density estimators of density function at an unknown point.

On Practical Efficiency of Locally Parametric Nonparametric Density Estimation Based on Local Likelihood Function

  • Kang, Kee-Hoon;Han, Jung-Hoon
    • Communications for Statistical Applications and Methods
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    • v.10 no.2
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    • pp.607-617
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    • 2003
  • This paper offers a practical comparison of efficiency between local likelihood approach and conventional kernel approach in density estimation. The local likelihood estimation procedure maximizes a kernel smoothed log-likelihood function with respect to a polynomial approximation of the log likelihood function. We use two types of data driven bandwidths for each method and compare the mean integrated squares for several densities. Numerical results reveal that local log-linear approach with simple plug-in bandwidth shows better performance comparing to the standard kernel approach in heavy tailed distribution. For normal mixture density cases, standard kernel estimator with the bandwidth in Sheather and Jones(1991) dominates the others in moderately large sample size.

Transformation in Kernel Density Estimation (변환(變換)을 이용(利用)한 커널함수추정추정법(函數推定推定法))

  • Seog, Kyung-Ha
    • 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.

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Mutual Information in Naive Bayes with Kernel Density Estimation (나이브 베이스에서의 커널 밀도 측정과 상호 정보량)

  • Xiang, Zhongliang;Yu, Xiangru;Kang, Dae-Ki
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2014.05a
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    • pp.86-88
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    • 2014
  • Naive Bayes (NB) assumption has some harmful effects in classification to the real world data. To relax this assumption, we now propose approach called Naive Bayes Mutual Information Attribute Weighting with Smooth Kernel Density Estimation (NBMIKDE) that combine the smooth kernel for attribute and attribute weighting method based on mutual information measure.

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Effects of Physical Factors on Computed Tomography Image Quality

  • Jeon, Min-Cheol;Han, Man-Seok;Jang, Jae-Uk;Kim, Dong-Young
    • Journal of Magnetics
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    • v.22 no.2
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    • pp.227-233
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    • 2017
  • The purpose of this study was to evaluate the effects of X-ray photon energy, tissue density, and the kernel essential for image reconstruction on the image quality by measuring HU and noise. Images were obtained by scanning the RMI density phantom within the CT device, and HU and noise were measured as follows: images were obtained by varying the tube voltages, the tube currents and eight different kernels. The greater the voltage-dependent change in the HU value but the noise was decreased. At all densities, changes in the tube current did not exert any significant influence on the HU value, whereas the noise value gradually decreased as the tube current increased. At all densities, changes in the kernel did not exert any significant influence on the HU value. The noise value gradually increased in the lower kernel range, but rapidly increased in the higher kernel range. HU is influenced by voltage and density, and noise is influenced by voltage, current, kernel, and density. This affects contrast resolution and spatial resolution.

A Note on Support Vector Density Estimation with Wavelets

  • Lee, Sung-Ho
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.2
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    • pp.411-418
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    • 2005
  • We review support vector and wavelet density estimation. The relationship between support vector and wavelet density estimation in reproducing kernel Hilbert space (RKHS) is investigated in order to use wavelets as a variety of support vector kernels in support vector density estimation.

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On the Equality of Two Distributions Based on Nonparametric Kernel Density Estimator

  • Kim, Dae-Hak;Oh, Kwang-Sik
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.2
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    • pp.247-255
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    • 2003
  • Hypothesis testing for the equality of two distributions were considered. Nonparametric kernel density estimates were used for testing equality of distributions. Cross-validatory choice of bandwidth was used in the kernel density estimation. Sampling distribution of considered test statistic were developed by resampling method, called the bootstrap. Small sample Monte Carlo simulation were conducted. Empirical power of considered tests were compared for variety distributions.

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