Abstract

In this paper, we consider an extension of the generalized class of recursive regression estimators to the case of strong mixing data. Then, we study the properties of these estimators and compare them with the well known Nadaraya-Watson estimator. The Bias, variance and Mean Integrated Square Error are computed explicitly. Using a selected bandwidth and a special stepsize, we showed that the proposed recursive estimators allowed us to obtain quite better results compared to the non-recursive regression estimator under α-mixing condition in terms of estimation error and much better in terms of computational costs.

中文翻译：

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α下的递归核回归估计——混合数据

摘要

在本文中，我们考虑将递归回归估计器的广义类扩展到强混合数据的情况。然后，我们研究这些估计量的性质，并将它们与著名的 Nadaraya-Watson 估计量进行比较。偏差、方差和平均积分平方误差是明确计算的。使用选定的带宽和特殊的步长，我们表明，与α混合条件下的非递归回归估计器相比，所提出的递归估计器在估计误差和计算成本方面比非递归回归估计器获得了更好的结果。