scholarly journals N-W Kernel Regression Estimation for Correlation Function of Bivariate Extremes Copula Function

2018 ◽  
Vol 07 (02) ◽  
pp. 234-240
Author(s):  
晓艺 蒋
Keyword(s):  
Correlation Function ◽  
Kernel Regression ◽  
Copula Function ◽  
Regression Estimation ◽  
Kernel Regression Estimation

Kernel Regression Estimation for Functional Data

2018 ◽  
Author(s):  
Frédéric Ferraty ◽  
Philippe Vieu
Keyword(s):  
Kernel Methods ◽  
Functional Data ◽  
Linear Functional ◽  
Kernel Smoothing ◽  
Kernel Regression ◽  
Functional Regression ◽  
Regression Estimation ◽  
Asymptotic Results ◽  
Kernel Regression Estimation ◽  
The Impact

This article provides an overview of recent nonparametric and semiparametric advances in kernel regression estimation for functional data. In particular, it considers the various statistical techniques based on kernel smoothing ideas that have recently been developed for functional regression estimation problems. The article first examines nonparametric functional regression modelling before discussing three popular functional regression estimates constructed by means of kernel ideas, namely: the Nadaraya-Watson convolution kernel estimate, the kNN functional estimate, and the local linear functional estimate. Uniform asymptotic results are then presented. The article proceeds by reviewing kernel methods in semiparametric functional regression such as single functional index regression and partial linear functional regression. It also looks at the use of kernels for additive functional regression and concludes by assessing the impact of kernel methods on practical real-data analysis involving functional (curves) datasets.

scholarly journals Random Noise Suppression of Magnetic Resonance Sounding Data with Intensive Sampling Sparse Reconstruction and Kernel Regression Estimation

2019 ◽  
Vol 11 (15) ◽  
pp. 1829 ◽  
Author(s):  
Yao ◽  
Zhang ◽  
Yu ◽  
Zhao ◽  
Sun
Keyword(s):  
Magnetic Resonance ◽  
Field Experiments ◽  
Noise Suppression ◽  
Random Noise ◽  
Kernel Regression ◽  
Sparse Reconstruction ◽  
Regression Estimation ◽  
Magnetic Resonance Sounding ◽  
Kernel Regression Estimation ◽  
Intensive Sampling

The magnetic resonance sounding (MRS) method is a non-invasive, efficient and advanced geophysical method for groundwater detection. However, the MRS signal received by the coil sensor is extremely susceptible to electromagnetic noise interference. In MRS data processing, random noise suppression of noisy MRS data is an important research aspect. We propose an approach for intensive sampling sparse reconstruction (ISSR) and kernel regression estimation (KRE) to suppress random noise. The approach is based on variable frequency sampling, numerical integration and statistical signal processing combined with kernel regression estimation. In order to realize the approach, we proposed three specific sparse reconstructions, namely rectangular sparse reconstruction, trapezoidal sparse reconstruction and Simpson sparse reconstruction. To solve the distortion of peaks and valleys after sparse reconstruction, we introduced the KRE to deal with the processed data by the ISSR. Further, the simulation and field experiments demonstrate that the ISSR-KRE approach is a feasible and effective way to suppress random noise. Besides, we find that rectangular sparse reconstruction and trapezoidal sparse reconstruction are superior to Simpson sparse reconstruction in terms of noise suppression effect, and sampling frequency is positively correlated with signal-to-noise improvement ratio (SNIR). In one case of field experiment, the standard deviation of noisy MRS data was reduced from 1200.80 nV to 570.01 nV by the ISSR-KRE approach. The proposed approach provides theoretical support for random noise suppression and contributes to the development of MRS instrument with low power consumption and high efficiency. In the future, we will integrate the approach into MRS instrument and attempt to utilize them to eliminate harmonic noise from power line.

scholarly journals Local adaptive smoothing in kernel regression estimation

2010 ◽  
Vol 80 (7-8) ◽  
pp. 540-547 ◽  
Author(s):  
Qi Zheng ◽  
K.B. Kulasekera ◽  
Colin Gallagher
Keyword(s):  
Kernel Regression ◽  
Regression Estimation ◽  
Adaptive Smoothing ◽  
Kernel Regression Estimation
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