We collect robust proposals given in the field of regression models with heteroscedastic errors. Our motivation stems from the fact that the practitioner frequently faces the confluence of two phenomena in the context of data analysis: non--linearity and heteroscedasticity. The impact of heteroscedasticity on the precision of the estimators is well--known, however the conjunction of these two phenomena makes handling outliers more difficult. An iterative procedure to estimate the parameters of a heteroscedastic non--linear model is considered. The studied estimators combine weighted $MM-$regression estimators, to control the impact of high leverage points, and a robust method to estimate the parameters of the variance function.

翻译：我们收集了异方差误差回归模型领域的稳健性方法。研究动机源于数据分析实践中研究者常面临的两种现象交织：非线性和异方差性。异方差性对估计量精度的影响已广为人知，但两种现象的并存使得异常值处理更为困难。本文考虑了一种迭代方法以估计异方差非线性模型的参数。所研究的估计量结合了加权$MM$回归估计量（用于控制高杠杆点的影响）和估计方差函数参数的稳健方法。