A new approach using the genetic algorithm for parameter estimation in multiple linear regression with long-tailed symmetric distributed error terms: An application to the Covid-19 data

Maximum likelihood (ML) estimators of the model parameters in multiple linear regression are obtained using genetic algorithm (GA) when the distribution of the error terms is long-tailed symmetric. We compare the efficiencies of the ML estimators obtained using GA with the corresponding ML estimators obtained using other iterative techniques via an extensive Monte Carlo simulation study. Robust confidence intervals based on modified ML estimators are used as the search space in GA. Our simulation study shows that GA outperforms traditional algorithms in most cases. Therefore, we suggest using GA to obtain the ML estimates of the multiple linear regression model parameters when the distribution of the error terms is LTS. Finally, real data of the Covid19 pandemic, a global health crisis in early 2020, is presented for illustrative purposes.

Automated summary

This study employs genetic algorithm to obtain maximum likelihood estimates in multiple linear regression, and finds that GA performs better than traditional algorithms when the error distribution is long-tailed symmetric. The use of robust confidence intervals based on modified ML estimators as the search space in GA improves parameter estimation efficiency, as indicated by the simulation results.