Relevant bibliographies by topics / Epanechnikov kernel

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Academic literature on the topic 'Epanechnikov kernel'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Epanechnikov kernel"

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Mesquita, D. P. P., J. P. P. Gomes, and A. H. Souza Junior. "Epanechnikov kernel for incomplete data." Electronics Letters 53, no. 21 (October 2017): 1408–10. http://dx.doi.org/10.1049/el.2017.0507.

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Chu, Chi-Yang, Daniel J. Henderson, and Christopher F. Parmeter. "On discrete Epanechnikov kernel functions." Computational Statistics & Data Analysis 116 (December 2017): 79–105. http://dx.doi.org/10.1016/j.csda.2017.07.003.

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Du, Xiang Ran, Hai Tao Liu, and Min Zhang. "Comparative Analysis on Kernel Based Probability Density Estimation." Applied Mechanics and Materials 543-547 (March 2014): 1655–58. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1655.

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In this paper, we compare the estimation performances of 7 different kernels (i.e., Uniform, Triangular, Epanechnikov, Biweight, Triweight, Cosine and Gaussian) when using them to conduct the probability density estimation with Parzen window method. We firstly analyze the efficiencies of these 7 kernels and then compare their estimation errors measured by mean squared error (MSE). The theoretical analysis and the experimental comparisons show that the mostly-used Gaussian kernel is not the best choice for the probability density estimation, of which the efficiency is low and estimation error is high. The derived conclusions give some guidelines for the selection of kernel in the practical application of probability density estimation.
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Karczewski, Maciej, and Andrzej Michalski. "The study and comparison of one-dimensional kernel estimators – a new approach. Part 2. A hydrology case study." ITM Web of Conferences 23 (2018): 00018. http://dx.doi.org/10.1051/itmconf/20182300018.

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The main purpose of this article is to present the numerical consequences of selected methods of kernel estimation, using the example of empirical data from a hydrological experiment [1, 2]. In the construction of kernel estimators we used two types of kernels – Gaussian and Epanechnikov – and several methods of selecting the optimal smoothing bandwidth (see Part 1), based on various statistical and analytical conditions [3–6]. Further analysis of the properties of kernel estimators is limited to eight characteristic estimators. To assess the effectiveness of the considered estimates and their similarity, we applied the distance measure of Marczewski and Steinhaus [7]. Theoretical and numerical considerations enable the development of an algorithm for the selection of locally most effective kernel estimators.
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IVAN, KOMANG CANDRA, I. WAYAN SUMARJAYA, and MADE SUSILAWATI. "ANALISIS MODEL REGRESI NONPARAMETRIK SIRKULAR-LINEAR BERGANDA." E-Jurnal Matematika 5, no. 2 (May 31, 2016): 52. http://dx.doi.org/10.24843/mtk.2016.v05.i02.p121.

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Circular data are data which the value in form of vector is circular data. Statistic analysis that is used in analyzing circular data is circular statistics analysis. In regression analysis, if any of predictor or response variables or both are circular then the regression analysis used is called circular regression analysis. Observation data in circular statistic which use direction and time units usually don’t satisfy all of the parametric assumptions, thus making nonparametric regression as a good solution. Nonparametric regression function estimation is using epanechnikov kernel estimator for the linier variables and von Mises kernel estimator for the circular variable. This study showed that the result of circular analysis by using circular descriptive statistic is better than common statistic. Multiple circular-linier nonparametric regressions with Epanechnikov and von Mises kernel estimator didn’t create estimation model explicitly as parametric regression does, but create estimation from its observation knots instead.
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Yu, Liang-ju, Gen-ke Yang, and Yue Chen. "Improved Independent Component Analysis Based on Epanechnikov Kernel Function." International Journal of Control and Automation 9, no. 7 (July 31, 2016): 147–58. http://dx.doi.org/10.14257/ijca.2016.9.7.14.

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Kalita, Jumi, and Pranita Sarmah. "Application of Epanechnikov kernel smoothing technique in disability data." International Journal of Intelligent Systems Design and Computing 1, no. 1/2 (2017): 198. http://dx.doi.org/10.1504/ijisdc.2017.082874.

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Kalita, Jumi, and Pranita Sarmah. "Application of Epanechnikov kernel smoothing technique in disability data." International Journal of Intelligent Systems Design and Computing 1, no. 1/2 (2017): 198. http://dx.doi.org/10.1504/ijisdc.2017.10003810.

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Czesak, Barbara, Renata Różycka-Czas, Tomasz Salata, Robert Dixon-Gough, and Józef Hernik. "Determining the Intangible: Detecting Land Abandonment at Local Scale." Remote Sensing 13, no. 6 (March 18, 2021): 1166. http://dx.doi.org/10.3390/rs13061166.

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Precisely determining agricultural land abandonment (ALA) in an area is still difficult, even with recent progress in data collection and analysis. It is especially difficult in fragmented areas that need more tailor-made methods. The aim of this research was to determine ALA using airborne laser scanning (ALS) data, which are available in Poland with 4 to 6 points per square metre resolution. ALS data were processed into heat maps and modified with chosen kernel functions: triweight and Epanechnikov. The results of ALS data processing were compared to the control method, i.e., visual interpretation of an orthophotomap. This study shows that ALS data modelled with kernel functions allow for a good identification of ALA. The accuracy of results shows 82% concordance as compared to the control method. When comparing triweight and Epanechnikov functions, higher accuracy was achieved when using the triweight function. The research shows that ALS data processing is a promising method of detection of ALA and could provide an alternative to well-known methods such as the analysis of satellite images.
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Karczewski, Maciej, and Andrzej Michalski. "The study and comparison of one-dimensional kernel estimators – a new approach. Part 1. Theory and methods." ITM Web of Conferences 23 (2018): 00017. http://dx.doi.org/10.1051/itmconf/20182300017.

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In this article we compare and examine the effectiveness of different kernel density estimates for some experimental data. For a given random sample X1, X2, …, Xn we present eight kernel estimators fn of the density function f with the Gaussian kernel and with the kernel given by Epanechnikov [1] using several methods: Silverman’s rule of thumb, the Sheather–Jones method, cross-validation methods, and other better-known plug-in methods [2–5]. To assess the effectiveness of the considered estimators and their similarity, we applied a distance measure for measurable and integrable functions [6]. All numerical calculations were performed for a set of experimental data recording groundwater level at a land reclamation facility (cf. [7–8]). The goal of the paper is to present a method that allows the study of local properties of the examined kernel estimators.
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Dissertations / Theses on the topic "Epanechnikov kernel"

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Mesquita, Diego Parente Paiva. "Machine Learning for incomplete data." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/25193.

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MESQUITA, Diego Parente Paiva. Machine Learning for incomplete data. 2017. 55 f. Dissertação (Mestrado em Ciência da Computação)-Universidade Federal do Ceará, Fortaleza, 2017.
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Methods based on basis functions (such as the sigmoid and q-Gaussian functions) and similarity measures (such as distances or kernel functions) are widely used in machine learning and related fields. These methods often take for granted that data is fully observed and are not equipped to handle incomplete data in an organic manner. This assumption is often flawed, as incomplete data is a fact in various domains such as medical diagnosis and sensor analytics. Therefore, one might find it useful to be able to estimate the value of these functions in the presence of partially observed data. We propose methodologies to estimate the Gaussian Kernel, the Euclidean Distance, the Epanechnikov kernel and arbitrary basis functions in the presence of possibly incomplete feature vectors. To obtain such estimates, the incomplete feature vectors are treated as continuous random variables and, based on that, we take the expected value of the transforms of interest.
Métodos baseados em funções de base (como as funções sigmoid e a q-Gaussian) e medidas de similaridade (como distâncias ou funções de kernel) são comuns em Aprendizado de Máquina e áreas correlatas. Comumente, no entanto, esses métodos não são equipados para utilizar dados incompletos de maneira orgânica. Isso pode ser visto como um impedimento, uma vez que dados parcialmente observados são comuns em vários domínios, como aplicações médicas e dados provenientes de sensores. Nesta dissertação, propomos metodologias para estimar o valor do kernel Gaussiano, da distância Euclidiana, do kernel Epanechnikov e de funções de base arbitrárias na presença de vetores possivelmente parcialmente observados. Para obter tais estimativas, os vetores incompletos são tratados como variáveis aleatórias contínuas e, baseado nisso, tomamos o valor esperado da transformada de interesse.
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Diaz, José Ignacio Valencia. "Modelagem não-paramétrica da dinâmica da taxa de juros instantânea utilizando contratos futuros da taxa média dos depósitos interfinanceiros de 1 dia (DI1)." reponame:Repositório Institucional do FGV, 2013. http://hdl.handle.net/10438/11130.

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Prediction models based on nonparametric estimation are in continuous development and have been permeating the quantitative community. Their main feature is that they do not consider as known a priori the form of the probability distributions functions (PDF), but allow the data to be used directly in order to build their own PDFs. In this work it is implemented the nonparametric pooled estimators from Sam and Jiang (2009) for drift and diffusion functions for the short rate diffusion process, by means of the use of yield series of different maturities provided by One Day Future Interbank Deposit contracts (ID1). The estimators are built from the perspective of kernel functions and they are optimized with a particular kernel format, in our case, Epanechnikov’s kernel, and with a smoothing parameter (bandwidth). Empiric experience indicates that the smoothing parameter is critical to find the probability density function that provides an optimal estimation in terms of MISE (Mean Integrated Squared Error) when testing the model with the traditional k-folds cross-validation method. Exceptions arise when the series do not have appropriate sizes, but the structural break of the diffusion process of the Brazilian interest short rate, since 2006, requires the reduction of the length of the series to the cost of reducing the predictive power of the model. This structural break represents the evolution of the Brazilian market, in an attempt to converge towards mature markets and it explains largely the unsatisfactory performance of the proposed estimator.
Modelos de predição baseados em estimações não-paramétricas continuam em desenvolvimento e têm permeado a comunidade quantitativa. Sua principal característica é que não consideram a priori distribuições de probabilidade conhecidas, mas permitem que os dados passados sirvam de base para a construção das próprias distribuições. Implementamos para o mercado brasileiro os estimadores agrupados não-paramétricos de Sam e Jiang (2009) para as funções de drift e de difusão do processo estocástico da taxa de juros instantânea, por meio do uso de séries de taxas de juros de diferentes maturidades fornecidas pelos contratos futuros de depósitos interfinanceiros de um dia (DI1). Os estimadores foram construídos sob a perspectiva da estimação por núcleos (kernels), que requer para a sua otimização um formato específico da função-núcleo. Neste trabalho, foi usado o núcleo de Epanechnikov, e um parâmetro de suavizamento (largura de banda), o qual é fundamental para encontrar a função de densidade de probabilidade ótima que forneça a estimação mais eficiente em termos do MISE (Mean Integrated Squared Error - Erro Quadrado Integrado Médio) no momento de testar o modelo com o tradicional método de validação cruzada de k-dobras. Ressalvas são feitas quando as séries não possuem os tamanhos adequados, mas a quebra estrutural do processo de difusão da taxa de juros brasileira, a partir do ano 2006, obriga à redução do tamanho das séries ao custo de reduzir o poder preditivo do modelo. A quebra estrutural representa um processo de amadurecimento do mercado brasileiro que provoca em grande medida o desempenho insatisfatório do estimador proposto.
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Book chapters on the topic "Epanechnikov kernel"

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González, Jorge, and Alina A. von Davier. "An Illustration of the Epanechnikov and Adaptive Continuization Methods in Kernel Equating." In Springer Proceedings in Mathematics & Statistics, 253–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56294-0_23.

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Bodyanskiy, Yevgeniy, Olena Vynokurova, and Oleksii Tyshchenko. "Hybrid Wavelet-Neuro-Fuzzy Systems of Computational Intelligence in Data Mining Tasks." In Handbook of Research on Machine Learning Innovations and Trends, 787–825. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2229-4.ch035.

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This work is devoted to synthesis of adaptive hybrid systems based on the Computational Intelligence (CI) methods (especially artificial neural networks (ANNs)) and the Group Method of Data Handling (GMDH) ideas to get new qualitative results in Data Mining, Intelligent Control and other scientific areas. The GMDH-artificial neural networks (GMDH-ANNs) are currently well-known. Their nodes are two-input N-Adalines. On the other hand, these ANNs can require a considerable number of hidden layers for a necessary approximation quality. Introduced Q-neurons can provide a higher quality using the quadratic approximation. Their main advantage is a high learning rate. Universal approximating properties of the GMDH-ANNs can be achieved with the help of compartmental R-neurons representing a two-input RBFN with the grid partitioning of the input variables' space. An adjustment procedure of synaptic weights as well as both centers and receptive fields is provided. At the same time, Epanechnikov kernels (their derivatives are linear to adjusted parameters) can be used instead of conventional Gauss functions in order to increase a learning process rate. More complex tasks deal with stochastic time series processing. This kind of tasks can be solved with the help of the introduced adaptive W-neurons (wavelets). Learning algorithms are characterized by both tracking and smoothing properties based on the quadratic learning criterion. Robust algorithms which eliminate an influence of abnormal outliers on the learning process are introduced too. Theoretical results are illustrated by multiple experiments that confirm the proposed approach's effectiveness.
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Conference papers on the topic "Epanechnikov kernel"

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Guo, Qingchang, Xiaojuan Chang, and Hongxia Chu. "Mean-Shift of Variable Window Based on the Epanechnikov Kernel." In 2007 International Conference on Mechatronics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/icma.2007.4303914.

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Oshiro, Masakuni, and Toshihiro Nishimura. "US image improvement using fuzzy Neural Network with Epanechnikov kernel." In IECON 2009 - 35th Annual Conference of IEEE Industrial Electronics (IECON). IEEE, 2009. http://dx.doi.org/10.1109/iecon.2009.5415342.

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Moraes, Caroline, Denis Fantinato, and Aline Neves. "An Epanechnikov Kernel Based Method for Source Separation in Post-Nonlinear Mixtures." In XXXVII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais. Sociedade Brasileira de Telecomunicações, 2019. http://dx.doi.org/10.14209/sbrt.2019.1570558558.

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Liu, Boning, Yan Zhao, Xiaomeng Jiang, and Shigang Wang. "An Image Coding Approach Based on Mixture-of-experts Regression Using Epanechnikov Kernel." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682374.

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Li, Dawei, Lihong Xu, and Yang Wu. "Improved CAMShift object tracking based on Epanechnikov Kernel Density Estimation and Kalman filter." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7979044.

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