Covariance matrices of S robust regression estimators

2021 ◽  
pp. 1-24
Author(s):  
S. Salini ◽  
F. Laurini ◽  
G. Morelli ◽  
M. Riani ◽  
A. Cerioli
Keyword(s):  
Robust Regression ◽  
Covariance Matrices ◽  
Regression Estimators

An analysis of constrained robust regression estimators

1984 ◽  
Vol 31 (2) ◽  
pp. 283-296 ◽  
Author(s):  
Ronald G. Askin ◽  
Douglas C. Montgomery
Keyword(s):  
Robust Regression ◽  
Regression Estimators

scholarly journals Minimum Covariance Determinant-Based Quantile Robust Regression-Type Estimators for Mean Parameter

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Usman Shahzad ◽  
Nadia H. Al-Noor ◽  
Noureen Afshan ◽  
David Anekeya Alilah ◽  
Muhammad Hanif ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Robust Regression ◽  
Real Life ◽  
Minimum Covariance Determinant ◽  
Mean Squared Errors ◽  
Regression Estimators ◽  
Chi Squared ◽  
Class Of Estimators ◽  
The Mean ◽  
Ratio Estimators

Robust regression tools are commonly used to develop regression-type ratio estimators with traditional measures of location whenever data are contaminated with outliers. Recently, the researchers extended this idea and developed regression-type ratio estimators through robust minimum covariance determinant (MCD) estimation. In this study, the quantile regression with MCD-based measures of location is utilized and a class of quantile regression-type mean estimators is proposed. The mean squared errors (MSEs) of the proposed estimators are also obtained. The proposed estimators are compared with the reviewed class of estimators through a simulation study. We also incorporated two real-life applications. To assess the presence of outliers in these real-life applications, the Dixon chi-squared test is used. It is found that the quantile regression estimators are performing better as compared to some existing estimators.

scholarly journals Computing Robust Regression Estimators: Developments since Dutter (1977)

2016 ◽  
Vol 41 (1) ◽  
Author(s):  
Moritz Gschwandtner ◽  
Peter Filzmoser
Keyword(s):  
High Efficiency ◽  
Robust Regression ◽  
Fast Algorithms ◽  
Software Packages ◽  
Regression Estimators ◽  
High Level ◽  
Increased Activity

The proposal ofMestimators for regression (Huber, 1973) and the development of an algorithm for its computation (Dutter, 1977) has lead to an increased activity for further research in this area. New regression estimators were introduced that combine a high level of robustness with high efficiency. Also fast algorithms have been developed and implemented in several software packages. We provide a review of the most important methods, and compare the performance of the algorithms implemented in R .

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