• Title/Summary/Keyword: kernel estimation

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Bootstrap estimation of long-run variance under strong dependence (장기간 의존 시계열에서 붓스트랩을 이용한 장기적 분산 추정)

  • Baek, Changryong;Kwon, Yong
    • The Korean Journal of Applied Statistics
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    • v.29 no.3
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    • pp.449-462
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    • 2016
  • This paper considers a long-run variance estimation using a block bootstrap method under strong dependence also known as long range dependence. We extend currently available methods in two ways. First, it extends bootstrap methods under short range dependence to long range dependence. Second, to accommodate the observation that strong dependence may come from deterministic trend plus noise models, we propose to utilize residuals obtained from the nonparametric kernel estimation with the bimodal kernel. The simulation study shows that our method works well; in addition, a data illustration is presented for practitioners.

Performance Comparison of Background Estimation in the Video (영상에서의 배경추정알고리즘 성능 비교)

  • Do, Jin-Kyu;Kim, Gyu-Yeong;Park, Jang-Sik;Kim, Hyun-Tae;Yu, Yun-Sik
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2011.05a
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    • pp.808-810
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    • 2011
  • The background estimation algorithms had a significant impact on the performance of image processing and recognition. In this paper, background estimation algorithms were analysis of complexity and performance as preprocessing of image recognition. It was evaluated the performance of Gaussian Running Average, Mixture of Gaussian, and KDE algorithm. The simulation results show that KDE algorithm outperforms compared to the other algorithms.

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A novel reliability analysis method based on Gaussian process classification for structures with discontinuous response

  • Zhang, Yibo;Sun, Zhili;Yan, Yutao;Yu, Zhenliang;Wang, Jian
    • Structural Engineering and Mechanics
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    • v.75 no.6
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    • pp.771-784
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    • 2020
  • Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.

Credibility estimation via kernel mixed effects model

  • Shim, Joo-Yong;Kim, Tae-Yoon;Lee, Sang-Yeol;Hwa, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.2
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    • pp.445-452
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    • 2009
  • Credibility models are actuarial tools to distribute premiums fairly among a heterogeneous group of policyholders. Many existing credibility models can be expressed as special cases of linear mixed effects models. In this paper we propose a nonlinear credibility regression model by reforming the linear mixed effects model through kernel machine. The proposed model can be seen as prediction method applicable in any setting where repeated measures are made for subjects with different risk levels. Experimental results are then presented which indicate the performance of the proposed estimating procedure.

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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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    • v.30 no.6
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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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ESTIMATION OF A MODIFIED INTEGRAL ASSOCIATED WITH A SPECIAL FUNCTION KERNEL OF FOX'S H-FUNCTION TYPE

  • Al-Omari, Shrideh Khalaf Qasem
    • Communications of the Korean Mathematical Society
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    • v.35 no.1
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    • pp.125-136
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    • 2020
  • In this article, we discuss classes of generalized functions for certain modified integral operator of Bessel-type involving Fox's H-function kernel. We employ a known differentiation formula of Fox's H-function to obtain the definition and properties of the distributional modified Bessel-type integral. Further, we derive a smoothness theorem for its kernel in a complete countably multi-normed space. On the other hand, using an appropriate class of convolution products, we derive axioms and establish spaces of modified Boehmians which are generalized distributions. On the defined spaces, we introduce addition, convolution, differentiation and scalar multiplication and further properties of the extended integral.

PSO-optimized Pareto and Nash equilibrium gaming-based power allocation technique for multistatic radar network

  • Harikala, Thoka;Narayana, Ravinutala Satya
    • ETRI Journal
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    • v.43 no.1
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    • pp.17-30
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    • 2021
  • At present, multiple input multiple output radars offer accurate target detection and better target parameter estimation with higher resolution in high-speed wireless communication systems. This study focuses primarily on power allocation to improve the performance of radars owing to the sparsity of targets in the spatial velocity domain. First, the radars are clustered using the kernel fuzzy C-means algorithm. Next, cooperative and noncooperative clusters are extracted based on the distance measured using the kernel fuzzy C-means algorithm. The power is allocated to cooperative clusters using the Pareto optimality particle swarm optimization algorithm. In addition, the Nash equilibrium particle swarm optimization algorithm is used for allocating power in the noncooperative clusters. The process of allocating power to cooperative and noncooperative clusters reduces the overall transmission power of the radars. In the experimental section, the proposed method obtained the power consumption of 0.014 to 0.0119 at K = 2, M = 3 and K = 2, M = 3, which is better compared to the existing methodologies-generalized Nash game and cooperative and noncooperative game theory.

A selective review of nonlinear sufficient dimension reduction

  • Sehun Jang;Jun Song
    • Communications for Statistical Applications and Methods
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    • v.31 no.2
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    • pp.247-262
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    • 2024
  • In this paper, we explore nonlinear sufficient dimension reduction (SDR) methods, with a primary focus on establishing a foundational framework that integrates various nonlinear SDR methods. We illustrate the generalized sliced inverse regression (GSIR) and the generalized sliced average variance estimation (GSAVE) which are fitted by the framework. Further, we delve into nonlinear extensions of inverse moments through the kernel trick, specifically examining the kernel sliced inverse regression (KSIR) and kernel canonical correlation analysis (KCCA), and explore their relationships within the established framework. We also briefly explain the nonlinear SDR for functional data. In addition, we present practical aspects such as algorithmic implementations. This paper concludes with remarks on the dimensionality problem of the target function class.

Efficient Kernel Based 3-D Source Localization via Tensor Completion

  • Lu, Shan;Zhang, Jun;Ma, Xianmin;Kan, Changju
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.1
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    • pp.206-221
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    • 2019
  • Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

Improving Sample Entropy Based on Nonparametric Quantile Estimation

  • Park, Sang-Un;Park, Dong-Ryeon
    • Communications for Statistical Applications and Methods
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    • v.18 no.4
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    • pp.457-465
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    • 2011
  • Sample entropy (Vasicek, 1976) has poor performance, and several nonparametric entropy estimators have been proposed as alternatives. In this paper, we consider a piecewise uniform density function based on quantiles, which enables us to evaluate entropy in each interval, and study the poor performance of the sample entropy in terms of the poor estimation of lower and upper quantiles. Then we propose some improved entropy estimators by simply modifying the quantile estimators, and compare their performances with some existing estimators.