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Communications in Statistics - Simulation and Computation Volume 52, 2023 - Issue 10
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Articles

New robust ridge estimators for the linear regression model with outliers

Abdul Majida Pakistan Bureau of Statistics, Multan, PakistanCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-3071-2610
ORCID Icon,
Muhammad Aminb Department of Statistics, University of Sargodha, Sargodha, PakistanORCID Iconhttps://orcid.org/0000-0002-7431-5756
ORCID Icon,
Muhammad Aslamc Department of Statistics, Bahauddin Zakariya University, Multan, PakistanORCID Iconhttps://orcid.org/0000-0003-2290-2307
ORCID Icon &
Shakeel Ahmadc Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan
Pages 4717-4738 | Received 16 Oct 2020, Accepted 05 Aug 2021, Published online: 09 Sep 2021

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Read on this site (3)

Danish Wasim, Sajjad Ahmad Khan & Muhammad Suhail. (2023) Modified robust ridge M-estimators for linear regression models: an application to tobacco data. Journal of Statistical Computation and Simulation 93:15, pages 2703-2724.
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Faiza Sami, Muhammad Moeen Butt & Muhammad Amin. (2023) Two parameter estimators for the Conway–Maxwell–Poisson regression model. Journal of Statistical Computation and Simulation 93:13, pages 2137-2157.
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Danish Wasim, Sajjad Ahmad Khan, Muhammad Suhail & Maha Shabbir. New penalized M-estimators in robust ridge regression: real life applications using sports and tobacco data. Communications in Statistics - Simulation and Computation 0:0, pages 1-20.
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Articles from other publishers (2)

Abdul Majid, Shakeel Ahmad, Muhammad Aslam & Muhammad Kashif. (2022) A robust Kibria–Lukman estimator for linear regression model to combat multicollinearity and outliers. Concurrency and Computation: Practice and Experience 35:4.
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Muhammad Suhail & Zoubir Layouni. (2022) Performance of some new robust ridge estimators. Performance of some new robust ridge estimators.

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