International Journal of Statistics and Applied Mathematics
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2023, Vol. 8, Issue 6, Part A

The robust regression estimators: Performance & evaluation


Author(s): P Anandhi and Dr. S Mohan Prabhu

Abstract: Ordinary Least Square (OLS) estimates for a linear model are extremely sensitive to odd values in the design space or outliers among unpredicted values. Even a single value can have a significant impact on parameter estimations. This study focuses on, reviews, and describes different existing and popular robust regression approaches, as well as compares their efficiency. Recent advances in robust regression algorithms are also presented.

DOI: 10.22271/maths.2023.v8.i6a.1444

Pages: 83-87 | Views: 292 | Downloads: 21

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International Journal of Statistics and Applied Mathematics
How to cite this article:
P Anandhi, Dr. S Mohan Prabhu. The robust regression estimators: Performance & evaluation. Int J Stat Appl Math 2023;8(6):83-87. DOI: 10.22271/maths.2023.v8.i6a.1444

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