Results 61 to 70 of about 411,514 (294)
Nonparametric bias reduction of diffusion function in stochastic volatility models
AIMS MathematicsThis paper proposed a novel bias correction method based on nonparametric kernel estimator of the diffusion function in stochastic volatility models. In the case of fixed time span, the asymptotic bias of kernel estimation and the proposed nonparametric ...
Yunyan Wang +2 more
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Interval Estimation of Value-at-Risk Based on Nonparametric Models
Econometrics, 2018 Value-at-Risk (VaR) has become the most important benchmark for measuring risk in portfolios of different types of financial instruments. However, as reported by many authors, estimating VaR is subject to a high level of uncertainty.
Hussein Khraibani +2 more
doaj +1 more source
Blind Deconvolution with Scale Ambiguity
Applied Sciences, 2020 Recent years have witnessed significant advances in single image deblurring due to the increasing popularity of electronic imaging equipment. Most existing blind image deblurring algorithms focus on designing distinctive image priors for blur kernel ...
Wanshu Fan +3 more
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Kernel Estimation of Relative Risk [PDF]
Bernoulli, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kelsall, Julia E., Diggle, Peter J.
openaire +3 more sources
Advanced Functional Materials, EarlyView.Biofabrication aims at providing innovative technologies and tools for the fabrication of tissue‐like constructs for tissue engineering and regenerative medicine applications. By integrating multiple biofabrication technologies, such as 3D (bio) printing with fiber fabrication methods, it would be more realistic to reconstruct native tissue's ...
Waseem Kitana +2 more
wiley +1 more source
A new family of kernels from the beta polynomial kernels with applications in density estimation
IJAIN (International Journal of Advances in Intelligent Informatics), 2020 One of the fundamental data analytics tools in statistical estimation is the non-parametric kernel method that involves probability estimates production.
Israel Uzuazor Siloko +2 more
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Sparse kernel density estimation technique based on zero-norm constraint [PDF]
, 2010 A sparse kernel density estimator is derived based on the zero-norm constraint, in which the zero-norm of the kernel weights is incorporated to enhance model sparsity.
Chen, S, Harris, C J, Hong, Xia
core +1 more source
Micropatterned Biphasic Printed Electrodes for High‐Fidelity on‐Skin Bioelectronics
Advanced Functional Materials, EarlyView.Micropatterned biphasic printed electrodes achieve unprecedented skin conformity and low impedance by combining liquid‐metal droplets with microstructured 3D lattices. This scalable approach enables high‐fidelity detection of ECG, EMG, and EEG signals, including alpha rhythms from the forehead, with long‐term comfort and stability.
Manuel Reis Carneiro +4 more
wiley +1 more source
Advanced Functional Materials, EarlyView.The study presents biodegradable and recyclable mixed‐matrix membranes (MMMs), hydrogels, and cryogels using luminescent nanoscale metal‐organic frameworks (nMOFs) and biopolymers. These bio‐nMOF‐MMMs combine europium‐based nMOFs as probes for the status of the materials with the biopolymers agar and gelatine and present alternatives to conventional ...
Moritz Maxeiner +4 more
wiley +1 more source
Super Resolution with Kernel Estimation and Dual Attention Mechanism
Information, 2020 Convolutional Neural Networks (CNN) have led to promising performance in super-resolution (SR). Most SR methods are trained and evaluated on predefined blur kernel datasets (e.g., bicubic).
Huan Liang +4 more
doaj +1 more source