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首页> 外文期刊>Journal of applied statistics >A high breakdown, high efficiency and bounded influence modified GM estimator based on support vector regression
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A high breakdown, high efficiency and bounded influence modified GM estimator based on support vector regression

机译：基于支持向量回归的高故障，高效率和有限影响力修正GM估计器

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Regression analysis aims to estimate the approximate relationship between the response variable and the explanatory variables. This can be done using classical methods such as ordinary least squares. Unfortunately, these methods are very sensitive to anomalous points, often called outliers, in the data set. The main contribution of this article is to propose a new version of the Generalized M-estimator that provides good resistance against vertical outliers and bad leverage points. The advantage of this method over the existing methods is that it does not minimize the weight of the good leverage points, and this increases the efficiency of this estimator. To achieve this goal, the fixed parameters support vector regression technique is used to identify and minimize the weight of outliers and bad leverage points. The effectiveness of the proposed estimator is investigated using real and simulated data sets.
机译：回归分析旨在估计响应变量和解释变量之间的近似关系。这可以使用经典方法（例如普通最小二乘法）来完成。不幸的是，这些方法对数据集中的异常点（通常称为异常值）非常敏感。本文的主要贡献是提出了一个新的广义M估计器，它可以很好地抵抗垂直离群值和不良的杠杆点。与现有方法相比，此方法的优点在于，它不会最小化良好杠杆点的权重，并且可以提高此估算器的效率。为了实现此目标，使用固定参数支持向量回归技术来识别异常值和不良杠杆点并将其最小化。使用真实和模拟的数据集研究了所提出的估计器的有效性。

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