• 제목/요약/키워드: kernel density

검색결과 301건 처리시간 0.026초

Advances in Data-Driven Bandwidth Selection

  • Park, Byeong U.
    • Journal of the Korean Statistical Society
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    • 제20권1호
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    • pp.1-28
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    • 1991
  • Considerable progress on the problem of data-driven bandwidth selection in kernel density estimation has been made recently. The goal of this paper is to provide an introduction to the methods currently available, with discussion at both a practical and a nontechnical theoretical level. The main setting considered here is global bandwidth kernel estimation, but some recent results on variable bandwidth kernel estimation are also included.

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Effects of Process Parameters on the Powder Characteristics of Uranium Oxide Kernel Prepared by Sol-gel Process (Sol-gel 공정을 이용한 UO2 kernel 제조에서 공정변수가 입자특성에 미치는 영향)

  • Kim, Yeon-Ku;Jeong, Kyung-Chai;Oh, Seung-Chul;Suhr, Dong-Soo;Cho, Moon-Sung
    • Journal of Powder Materials
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    • 제16권4호
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    • pp.254-261
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    • 2009
  • In this study, we investigated the unit process parameters in spherical $UO_2$ kernel preparation. Nearly perfect spherical $UO_3$ microspheres were obtained from the 0.6M of U-concentration in the broth solution, and the microstructure of the $UO_2$ kernel appeared the good results in the calcining, reducing, and sintering processes. For good sphericity, high density, suitable microstructure, and no-crack final $UO_2$ microspheres, the temperature control range in calcination process was $300{\sim}450^{\circ}C$, and the microstructure, the pore structure, and the density of $UO_2$ kernel could be controlled in this temperature range. Also, the concentration changes of the ageing solution in aging step were not effective factor in the gelation of the liquid droplets, but the temperature change of the ageing solution was very sensitive for the final ADU gel particles.

On Teaching of Computer-Software Field Using Smoothing Methodology (평활 방법론이 적용될 수 있는 컴퓨터-소프트웨어 교육분야 제안)

  • Lee Seung-Woo
    • Journal for History of Mathematics
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    • 제19권3호
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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.

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BERRY-ESSEEN BOUNDS OF RECURSIVE KERNEL ESTIMATOR OF DENSITY UNDER STRONG MIXING ASSUMPTIONS

  • Liu, Yu-Xiao;Niu, Si-Li
    • Bulletin of the Korean Mathematical Society
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    • 제54권1호
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    • pp.343-358
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    • 2017
  • Let {$X_i$} be a sequence of stationary ${\alpha}-mixing$ random variables with probability density function f(x). The recursive kernel estimators of f(x) are defined by $$\hat{f}_n(x)={\frac{1}{n\sqrt{b_n}}{\sum_{j=1}^{n}}b_j{^{-\frac{1}{2}}K(\frac{x-X_j}{b_j})\;and\;{\tilde{f}}_n(x)={\frac{1}{n}}{\sum_{j=1}^{n}}{\frac{1}{b_j}}K(\frac{x-X_j}{b_j})$$, where 0 < $b_n{\rightarrow}0$ is bandwith and K is some kernel function. Under appropriate conditions, we establish the Berry-Esseen bounds for these estimators of f(x), which show the convergence rates of asymptotic normality of the estimators.

How to Measure Nonlinear Dependence in Hydrologic Time Series (시계열 수문자료의 비선형 상관관계)

  • Mun, Yeong-Il
    • Journal of Korea Water Resources Association
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    • 제30권6호
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    • pp.641-648
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    • 1997
  • Mutual information is useful for analyzing nonlinear dependence in time series in much the same way as correlation is used to characterize linear dependence. We use multivariate kernel density estimators for the estimation of mutual information at different time lags for single and multiple time series. This approach is tested on a variety of hydrologic data sets, and suggested an appropriate delay time $ au$ at which the mutual information is almost zerothen multi-dimensional phase portraits could be constructed from measurements of a single scalar time series.

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Kernel Inference on the Inverse Weibull Distribution

  • Maswadah, M.
    • Communications for Statistical Applications and Methods
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    • 제13권3호
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    • pp.503-512
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    • 2006
  • In this paper, the Inverse Weibull distribution parameters have been estimated using a new estimation technique based on the non-parametric kernel density function that introduced as an alternative and reliable technique for estimation in life testing models. This technique will require bootstrapping from a set of sample observations for constructing the density functions of pivotal quantities and thus the confidence intervals for the distribution parameters. The performances of this technique have been studied comparing to the conditional inference on the basis of the mean lengths and the covering percentage of the confidence intervals, via Monte Carlo simulations. The simulation results indicated the robustness of the proposed method that yield reasonably accurate inferences even with fewer bootstrap replications and it is easy to be used than the conditional approach. Finally, a numerical example is given to illustrate the densities and the inferential methods developed in this paper.

Initial Prototype Selection in Fuzzy C-Means Using Kernel Density Estimation (커널 밀도 추정을 이용한 Fuzzy C-means의 초기 원형 설정)

  • Cho, Hyun-Hak;Heo, Gyeong-Yong;Kim, Kwang-Beak
    • Proceedings of the Korean Society of Computer Information Conference
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    • 한국컴퓨터정보학회 2011년도 제43차 동계학술발표논문집 19권1호
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    • pp.85-88
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    • 2011
  • Fuzzy C-Means (FCM) 알고리듬은 가장 널리 사용되는 군집화 알고리듬 중 하나로 다양한 응용 분야에서 사용되고 있다. 하지만 FCM은 여러 가지 문제점을 가지고 있으며 초기 원형 설정이 그 중 하나이다. FCM은 국부 최적해에 수렴하므로 초기 원형 설정에 따라 클러스터링 결과가 달라진다. 이 논문에서는 이러한 FCM의 초기 원형 설정 문제를 개선하기 위하여 커널밀도 추정 (kernel density estimation) 기법을 활용하는 방법을 제안한다. 제안한 방법에서는 먼저 커널 밀도 추정을 수행한 후 밀도가 높은 지역에 클러스터의 초기 원형을 설정하고 원형이 설정된 영역의 밀도를 감소시키는 과정을 반복함으로써 효율적으로 초기 원형을 설정할 수 있다. 제안된 방법이 일반적으로 사용되는 무작위 초기화 방법에 비해 효율적이라는 사실은 실험결과를 통해 확인할 수 있다.

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Distributed Channel Allocation Using Kernel Density Estimation in Cognitive Radio Networks

  • Ahmed, M. Ejaz;Kim, Joo Seuk;Mao, Runkun;Song, Ju Bin;Li, Husheng
    • ETRI Journal
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    • 제34권5호
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    • pp.771-774
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    • 2012
  • Typical channel allocation algorithms for secondary users do not include processes to reduce the frequency of switching from one channel to another caused by random interruptions by primary users, which results in high packet drops and delays. In this letter, with the purpose of decreasing the number of switches made between channels, we propose a nonparametric channel allocation algorithm that uses robust kernel density estimation to effectively schedule idle channel resources. Experiment and simulation results demonstrate that the proposed algorithm outperforms both random and parametric channel allocation algorithms in terms of throughput and packet drops.

A Berry-Esseen Type Bound in Kernel Density Estimation for a Random Left-Truncation Model

  • Asghari, P.;Fakoor, V.;Sarmad, M.
    • Communications for Statistical Applications and Methods
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    • 제21권2호
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    • pp.115-124
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    • 2014
  • In this paper we derive a Berry-Esseen type bound for the kernel density estimator of a random left truncated model, in which each datum (Y) is randomly left truncated and is sampled if $Y{\geq}T$, where T is the truncation random variable with an unknown distribution. This unknown distribution is estimated with the Lynden-Bell estimator. In particular the normal approximation rate, by choice of the bandwidth, is shown to be close to $n^{-1/6}$ modulo logarithmic term. We have also investigated this normal approximation rate via a simulation study.

An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering

  • Frigui, Hichem;Bchir, Ouiem;Baili, Naouel
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제13권4호
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    • pp.254-268
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    • 2013
  • For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.