Jackknife Kibria-Lukman M-Estimator: Simulation and Application

Authors

  • Segun L. Jegede
    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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Published

2022-05-29

How to Cite

Jackknife Kibria-Lukman M-Estimator: Simulation and Application. (2022). Journal of the Nigerian Society of Physical Sciences, 4(2), 251-264. https://doi.org/10.46481/jnsps.2022.664

Issue

Volume 4, Issue 2, May 2022

Section

Original Research
Copyright & Licensing

Copyright (c) 2022 Journal of the Nigerian Society of Physical Sciences

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Jackknife Kibria-Lukman M-Estimator: Simulation and Application. (2022). Journal of the Nigerian Society of Physical Sciences, 4(2), 251-264. https://doi.org/10.46481/jnsps.2022.664

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