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Central node identification via weighted kernel density estimation
The detection of central nodes in a network is a fundamental task in network science and graph data analysis. During the past decades, numerous...
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Iterative kernel density estimation from noisy-dependent observations
We consider the nonparametric estimation of the density function of an underlying random variable from a sequence of strongly mixing noisy...
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Quantum kernel estimation-based quantum support vector regression
Quantum machine learning endeavors to exploit quantum mechanical effects like superposition, entanglement, and interference to enhance the...
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A reliable data-based smoothing parameter selection method for circular kernel estimation
A new data-based smoothing parameter for circular kernel density (and its derivatives) estimation is proposed. Following the plug-in ideas, unknown...
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Kernel Quantile Estimation
From kernel distribution function, quantile estimators can be defined naturally. Using the kernel estimator of the p-th quantile of a distribution...
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The adaptive kernel-based extreme learning machine for state of charge estimation
The state of charge (SOC) is a key factor in the battery management, and the accuracy of its estimation plays an important role in battery-life...
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Estimating Species Abundance from Presence–Absence Maps by Kernel Estimation
We present a novel method for estimating species abundance using presence–absence maps. Our approach takes the spatial context into consideration,...
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A blind image super-resolution network guided by kernel estimation and structural prior knowledge
The goal of blind image super-resolution (BISR) is to recover the corresponding high-resolution image from a given low-resolution image with unknown...
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An adaptive method for bandwidth selection in circular kernel density estimation
Kernel density estimations of circular data are an effective type of nonparametric estimation. The performance of these estimations depends...
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Automatic beam optimization method for scanning electron microscopy based on electron beam Kernel estimation
Scanning Electron Microscopy (SEM) leverages electron wavelengths for nanoscale imaging, necessitating precise parameter adjustments like focus,...
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Kernel density estimation by stagewise algorithm with a simple dictionary
This study proposes multivariate kernel density estimation by stagewise minimization algorithm based on U -divergence and a simple dictionary. The...
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Class specific nullspace marginal discriminant analysis with overfitting-prevention kernel estimation for hand trajectory recognitions
Hand trajectories are widely used for gesture recognition, action analysis, and sign language translation. Effective hand trajectory feature...
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Load Spectra Extrapolation by Bandwidth-Optimized Kernel Density Estimation Based on DBSCAN Algorithm
Load spectra extrapolation is the basis of fatigue analysis and life prediction in engineering. This paper extrapolates the loads based on the kernel...
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Unfolded deep kernel estimation-attention UNet-based retinal image segmentation
Retinal vessel segmentation is a critical process in the automated inquiry of fundus images to screen and diagnose diabetic retinopathy. It is a...
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Monthly runoff time series interval prediction based on WOA-VMD-LSTM using non-parametric kernel density estimation
Logical development and effective use of water resources depend heavily on the practicability of runoff forecast. A monthly runoff interval...
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Statistical inference using regularized M-estimation in the reproducing kernel Hilbert space for handling missing data
Imputation is a popular technique for handling missing data. We address a nonparametric imputation using the regularized M-estimation techniques in...
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Reduction of Potential Boundary Bias in Kernel Cumulative Distribution Estimation in Univariate and Multivariate Settings
We propose a new method for nonparametric estimation of a probability distribution and its endpoint. As is well known, the kernel distribution...
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A New Kernel Density Estimation-Based Entropic Isometric Feature Map** for Unsupervised Metric Learning
Metric learning consists of designing adaptive distance functions that are well-suited to a specific dataset. Such tailored distance functions aim to...
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Nonparametric Bayesian online change point detection using kernel density estimation with nonparametric hazard function
This paper aims to develop Bayesian online change point detection (BOCD), a parametric change point detection method, into a nonparametric method to...
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Kernel-based time-varying IV estimation: handle with care
Giraitis et al. (J Econom 224(2):394–415, 2021) proposed a kernel-based time-varying coefficients IV estimator. By using entirely different code, we...
