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A review on robust M-estimators for regression analysis
Regression analysis constitutes an important tool for investigating the effect of explanatory
variables on response variables. When outliers and bias errors are present, the weighted …
variables on response variables. When outliers and bias errors are present, the weighted …
Enhancing performance in the presence of outliers with redescending M-estimators
In real-life situations, we have to analyze the data that contains the atypical observations,
and the presence of outliers has adverse effects on the performance of ordinary least square …
and the presence of outliers has adverse effects on the performance of ordinary least square …
Improved regression in ratio type estimators based on robust M-estimation
In this article, a new robust ratio type estimator using the Uk's redescending M-estimator is
proposed for the estimation of the finite population mean in the simple random sampling …
proposed for the estimation of the finite population mean in the simple random sampling …
Color-based image segmentation by means of a robust intuitionistic fuzzy c-means algorithm
To yield well-suited image segmentation results, conventional clustering algorithms depend
on customized hand-crafted features as well as an appropriate initialization process. This …
on customized hand-crafted features as well as an appropriate initialization process. This …
Robust RFI excision for pulsar signals by a novel nonlinear m-type estimator with an application to pulsar timing
H Shan - The Astrophysical Journal, 2023 - iopscience.iop.org
Radio frequency interference (RFI) mitigation for pulsar signals is a long perplexing issue in
astrophysical measurements. Linear mitigation methods are often criticized for limited RFI …
astrophysical measurements. Linear mitigation methods are often criticized for limited RFI …
A redescending M-estimator approach for outlier-resilient modeling
The OLS model is built on the assumption of normality in the distribution of error terms.
However, this assumption can be easily violated, especially when there are outliers in the …
However, this assumption can be easily violated, especially when there are outliers in the …
Robust Extreme Learning Machine Using New Activation and Loss Functions Based on M‐Estimation for Regression and Classification
This paper provides an analysis of the combining effect of novel activation function and loss
function based on M‐estimation in application to extreme learning machine (ELM), a feed …
function based on M‐estimation in application to extreme learning machine (ELM), a feed …
An efficient nonlinear approach for removing fixed-value impulse noise from grayscale images
Removal of salt and pepper noise has been one of the most interesting researches in the
field of image preprocessing tasks; it has two simultaneous stringent demands: the …
field of image preprocessing tasks; it has two simultaneous stringent demands: the …
M-estimators in regression models
Regression analysis plays a vital role in many areas of science. Almost all regression
analyses rely on the method of least squares for estimation of the parameters in the model …
analyses rely on the method of least squares for estimation of the parameters in the model …
[PDF][PDF] Redescending M-estimator for robust regression
In the linear regression problem, redescending M-estimators are used as an alternative
method to the ordinary least square method when there are outliers in the data. Using the …
method to the ordinary least square method when there are outliers in the data. Using the …