Results 241 to 250 of about 120,097 (295)

TSSS: A Novel Triangulated Spherical Spline Smoothing for Surface-Based Data. [PDF]

J Nonparametr Stat
Gu Z, Yu S, Wang G, Lai MJ, Wang L.
europepmc   +1 more source

Deep bayesian neural networks for UWB phase error correction in positioning systems. [PDF]

Sci Rep
Li J, Yu B, Yang M, Zhao J, Cui S, Tian R, Bao Y.   +6 more
europepmc   +1 more source

Riemannian geometry boosts functional near-infrared spectroscopy-based brain-state classification accuracy. [PDF]

Neurophotonics
Näher T, Bastian L, Vorreuther A, Fries P, Goebel R, Sorger B.   +5 more
europepmc   +1 more source

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Robust kernels for kernel density estimation

Economics Letters, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Shaoping, Li, Ang, Wen, Kuangyu, Wu, Ximing   +3 more
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Generalized Kernel Density Estimator

Theory of Probability & Its Applications, 2000
Summary: We introduce a new class of nonparametric density estimators. It includes the classical kernel density estimators as well as the popular Abramson's estimator. We show that the generalized estimators may perform much better than the classical one if the distribution has a heavy tail.
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Online Discriminative Kernel Density Estimator With Gaussian Kernels

IEEE Transactions on Cybernetics, 2014
We propose a new method for a supervised online estimation of probabilistic discriminative models for classification tasks. The method estimates the class distributions from a stream of data in the form of Gaussian mixture models (GMMs). The reconstructive updates of the distributions are based on the recently proposed online kernel density estimator ...
Matej, Kristan, Ales, Leonardis
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VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATES

Australian Journal of Statistics, 1990
SummaryThe term “variable kernel density estimate” is sometimes used to mean a kernel density estimate employing a different bandwidth for each data point, and sometimes to denote a kernel density estimate with bandwidth a function of estimation location.
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Bootstrapping kernel spectral density estimates with kernel bandwidth estimation

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004
We address the problem of confidence interval estimation of spectral densities using the bootstrap. Of special interest is the choice of the kernel global bandwidth. First, we investigate resampling based techniques for the choice of the bandwidth.
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