Yalçınkaya Abdullah, Balay İklim Gedik, Şenoǧlu Birdal
Department of Statistics, Ankara University, 06100, Ankara, Turkey.
Business School, Ankara Yıldırım Beyazıt University, 06760, Ankara, Turkey.
Chemometr Intell Lab Syst. 2021 Sep 15;216:104372. doi: 10.1016/j.chemolab.2021.104372. Epub 2021 Jun 29.
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 Covid-19 pandemic, a global health crisis in early 2020, is presented for illustrative purposes.
当误差项的分布为长尾对称时，使用遗传算法（GA）获得多元线性回归模型参数的最大似然（ML）估计量。我们通过广泛的蒙特卡罗模拟研究，比较了使用GA获得的ML估计量与使用其他迭代技术获得的相应ML估计量的效率。基于修正ML估计量的稳健置信区间被用作GA中的搜索空间。我们的模拟研究表明，在大多数情况下，GA优于传统算法。因此，我们建议当误差项的分布为长尾对称时，使用GA来获得多元线性回归模型参数的ML估计值。最后，给出了2020年初全球健康危机——新冠疫情的真实数据，以供说明之用。
