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A useful tool for statistical estimation: genetic algorithms

机译:统计估计的有用工具:遗传算法

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In this article, we introduce genetic algorithms (GAs) as a viable tool in estimating parameters in a wide array of statistical models. We performed simulation studies that compared the bias and variance of GAs with classical tools, namely, the steepest descent, Gauss-Newton, Levenberg-Marquardt and don't use derivative methods. In our simulation studies, we used the least squares criterion as the optimizing function. The performance of the GAs and classical methods were compared under the. logistic regression model; non-linear Gaussian model and non-linear non-Gaussian model. We report that the GAs' performance is competitive to the classical methods under these three models.
机译:在本文中,我们介绍了遗传算法(GA),它是一种在广泛的统计模型中估算参数的可行工具。我们进行了模拟研究,将GA的偏差和方差与经典工具(即最速下降的高斯牛顿,Levenberg-Marquardt)进行了比较,并且不使用导数方法。在我们的仿真研究中,我们使用最小二乘准则作为优化函数。将GA和经典方法的性能进行了比较。逻辑回归模型非线性高斯模型和非线性非高斯模型。我们报告说,在这三个模型下,GA的性能与传统方法相比具有竞争力。

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