Jackknife Kibria-Lukman M-Estimator: Simulation and Application

The ordinary least square (OLS) method is very efficient in estimating the regression parameters in a linear regression model under classical assumptions. If the model contains outliers, the performance of the OLS estimator becomes imprecise. Multicollinearity is another issue that can reduce the pe...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of Nigerian Society of Physical Sciences Vol. 4; no. 2; pp. 251 - 264
Main Authors:	Jegede, Segun L., Lukman, Adewale F., Ayinde, Kayode, Odeniyi, Kehinde A.
Format:	Journal Article
Language:	English
Published:	Nigerian Society of Physical Sciences 01-05-2022
Subjects:	Jackknife Kibria-Lukman M-estimator Monte Carlo Simulation Multicollinearity Outliers Robust
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	The ordinary least square (OLS) method is very efficient in estimating the regression parameters in a linear regression model under classical assumptions. If the model contains outliers, the performance of the OLS estimator becomes imprecise. Multicollinearity is another issue that can reduce the performance of the OLS estimator. This study proposed the Robust Jackknife Kibria-Lukman (RJKL) estimator based on the M-estimator to deal with multicollinearity and outliers. We examine the superiority of the estimator over existing estimators using theoretical proofs and Monte Carlo simulations. We put the estimator to the test once more using real-world data. We observed that the estimator performs better than the existing estimators.
ISSN:	2714-2817 2714-4704
DOI:	10.46481/jnsps.2022.664