Jackknife Kibria-Lukman M-Estimator: Simulation and Application

Segun L. Jegede; Adewale F. Lukman; Kayode  Ayinde; Kehinde A. Odeniyi

doi:10.46481/jnsps.2022.664

Authors

Segun L. Jegede
sjegede@kent.edu
Department of Mathematical Sciences, Kent State University https://orcid.org/0000-0002-9973-1377
Adewale F. Lukman Department of Mathematics, University of Medical Sciences, Nigeria; Institute of Mathematics, Eotvos Lorand University, Hungary https://orcid.org/0000-0003-2881-1297
Kayode Ayinde Department of Statistics, Federal University of Technology, Akure, Nigeria
Kehinde A. Odeniyi Department of Agricultural Economics and Agribusiness Management, Osun State University, Nigeria.

Keywords:

Jackknife Kibria-Lukman, M-estimator, Monte Carlo Simulation, Multicollinearity, Outliers, Robust

Abstract

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.

Dimensions

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