Kernel regression estimation with errors-in-variables for random fields
Sign Up to like & getrecommendations! Published in 2019 at "Afrika Matematika"
DOI: 10.1007/s13370-019-00654-7
Abstract: In this paper, we investigate kernel regression estimation when the data are contaminated by measurement errors in the context of random fields. We establish sharp rate of weak and strong convergence of the kernel regression… read more here.
Keywords: regression; random fields; kernel regression; regression estimation ... See more keywords
Modified kernel regression method for the denoising of X-ray pulsar profiles
Sign Up to like & getrecommendations! Published in 2018 at "Advances in Space Research"
DOI: 10.1016/j.asr.2018.04.027
Abstract: Abstract In this paper, a modified kernel regression algorithm is proposed to reduce the noise of pulsar profiles autonomously. Taking advantage of the classical autonomous kernel regression the presented algorithm based on the second-order derivative… read more here.
Keywords: pulsar profiles; kernel regression; regression; modified kernel ... See more keywords
Pulsar profile denoising using kernel regression based on maximum correntropy criterion
Sign Up to like & getrecommendations! Published in 2017 at "Optik"
DOI: 10.1016/j.ijleo.2016.10.132
Abstract: Abstract A pulsar profile denoising method using kernel regression based on maximum correntropy criterion is proposed. This method uses the kernel regression to reduce the human visual inspection inescapable in the current profile denoising methods… read more here.
Keywords: kernel regression; regression; maximum correntropy; profile denoising ... See more keywords
An adaptive kernel regression method for 3D ultrasound reconstruction using speckle prior and parallel GPU implementation
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2017.06.014
Abstract: Abstract Freehand three-dimensional (3D) ultrasound imaging is an attractive research area because it is capable of providing large field of view and high in-plane resolution image to allow better illustration of complex anatomy structures. However,… read more here.
Keywords: reconstruction; adaptive kernel; kernel regression; image ... See more keywords
Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Chemical Theory and Computation"
DOI: 10.1021/acs.jctc.1c00813
Abstract: The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of… read more here.
Keywords: neural network; network approaches; regression; kernel regression ... See more keywords
Convergence rate of the kernel regression estimator for associated and truncated data
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Nonparametric Statistics"
DOI: 10.1080/10485252.2017.1303059
Abstract: ABSTRACT This paper studies the behaviour of the kernel estimator of the regression function for associated data in the random left truncated model. The uniform strong consistency rate over a real compact set of the… read more here.
Keywords: kernel regression; regression; estimator; rate kernel ... See more keywords
Short-Term Forecasting of Electricity Spot Prices Containing Random Spikes Using a Time-Varying Autoregressive Model Combined With Kernel Regression
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2019.2911700
Abstract: Forecasting spot prices of electricity is challenging because it not only contains seasonal variations, but also random, abrupt spikes, which depend on market conditions and network contingencies. In this paper, a hybrid model has been… read more here.
Keywords: spot prices; kernel regression; model; time varying ... See more keywords
Kernel Regression for Matrix-Variate Gaussian Distributed Signals Over Sample Graphs
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2022.3202035
Abstract: Recent advances of kernel regression assume that target signals lie over a feature graph such that their values can be predicted with the assistance of the graph learned from training data. In this article, we… read more here.
Keywords: regression; kernel regression; graph; matrix variate ... See more keywords
A Kernel Regression Procedure in the 3D Shape Space with an Application to Online Sales of Children’s Wear
Sign Up to like & getrecommendations! Published in 2019 at "Statistical Science"
DOI: 10.1214/18-sts675
Abstract: This paper is focused on kernel regression when the response variable is the shape of a 3D object represented by a configuration matrix of landmarks. Regression methods on this shape space are not trivial because… read more here.
Keywords: regression; application online; kernel regression; shape space ... See more keywords
Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models
Sign Up to like & getrecommendations! Published in 2019 at "Metabolites"
DOI: 10.3390/metabo9080160
Abstract: In small molecule identification from tandem mass (MS/MS) spectra, input–output kernel regression (IOKR) currently provides the state-of-the-art combination of fast training and prediction and high identification rates. The IOKR approach can be simply understood as… read more here.
Keywords: molecule; kernel regression; molecule identification; small molecule ... See more keywords