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Nonparametric Density Estimation with Adaptive, Anisotropic Kernels for Human Motion Tracking
2007In this paper, we suggest to model priors on human motion by means of nonparametric kernel densities. Kernel densities avoid assumptions on the shape of the underlying distribution and let the data speak for themselves. In general, kernel density estimators suffer from the problem known as the curse of dimensionality, i.e., the amount of data required ...
Thomas Brox +3 more
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Salient object extraction based on nonparametric kernel density estimation
IET Conference on Wireless, Mobile and Sensor Networks 2007 (CCWMSN07), 2007A major problem in content-based image retrieve (CBIR) is how to extract the perceptually salient object in an image. In this paper, we propose an efficient approach for automatic extracting the salient objects. First, an input image is segmented into homogeneous regions based on nonparametric kernel density estimation (NKDE), and then different ...
null Weiwei Li +3 more
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Application of Nonparametric Kernel Density Estimation in Hongkong Stock Market
Applied Mechanics and Materials, 2011A new method, non-parametric kernel density, is used to research the distribution function of HangSeng index returns. The new method can not only depict the character of peak and fat tails of stock returns, but also capture the market risk better than normal distribution. Further more, more accurate conclusions are concluded.
Yu Ling Wang, Jing Wang
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A Complete Efficient FFT-Based Algorithm for Nonparametric Kernel Density Estimation
2017Multivariate kernel density estimation (KDE) is a very important statistical technique in exploratory data analysis. Research on high performance KDE is still an open research problem. One of the most elegant and efficient approach utilizes the Fast Fourier Transform.
Jaroslaw Gramacki, Artur Gramacki
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Nonparametric Kernel Spatial Density Estimation on Riemannianmanifolds
SSRN Electronic Journal, 2022Salah khardani +2 more
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Nonparametric -sample test based on kernel density estimator for paired design
Computational Statistics & Data Analysis, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Nonparametric entropy estimation using kernel densities.
Methods in enzymology, 2010The entropy of experimental data from the biological and medical sciences provides additional information over summary statistics. Calculating entropy involves estimates of probability density functions, which can be effectively accomplished using kernel density methods.
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