Modified Robust Ridge M-Estimators in Two-Parameter Ridge Regression Model | Zendy

Seyab Yasin | Zendy; Sultan Salem | Zendy; Hamdi Ayed | Zendy; Shahid Kamal | Zendy; Muhammad Suhail | Zendy; Yousaf Ali Khan | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Modified Robust Ridge M-Estimators in Two-Parameter Ridge Regression Model

Author(s) -

Seyab Yasin,

Sultan Salem,

Hamdi Ayed,

Shahid Kamal,

Muhammad Suhail,

Yousaf Ali Khan

Publication year - 2021

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2021/1845914

Subject(s) - multicollinearity , estimator , ridge , mean squared error , mathematics , outlier , monte carlo method , robust regression , statistics , regression , regression analysis , geology , paleontology

The methods of two-parameter ridge and ordinary ridge regression are very sensitive to the presence of the joint problem of multicollinearity and outliers in the y-direction. To overcome this problem, modified robust ridge M-estimators are proposed. The new estimators are then compared with the existing ones by means of extensive Monte Carlo simulations. According to mean squared error (MSE) criterion, the new estimators outperform the least square estimator, ridge regression estimator, and two-parameter ridge estimator in many considered scenarios. Two numerical examples are also presented to illustrate the simulation results.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research