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Approximating the projection depth median of dimensions p 3

机译:近似尺寸p 3的投影深度中位数

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摘要

As a multivariate generalization of the univariate median, projection depth median (PM) is unique, and enjoys a very high breakdown point, much higher than its affine equivariant competitors such as halfspace depth median. Nevertheless, its computation is challenging. Until now PM can only be exactly computed efficiently for bivariate data. In this article, we develop an algorithm to approximate PM in higher dimensions. Some data examples indicate that the proposed algorithm performs well in terms of both accuracy and efficiency. As an application, we investigate the finite sample relative efficiency of PM by utilizing the Matlab implementation of this algorithm.
机译:作为单变量中位数的多变量泛化,投影深度中位数(PM)是唯一的,并且具有很高的分解点,远高于其仿射等变量竞争对手(如半空间深度中位数)。然而,其计算具有挑战性。到目前为止,只能对双变量数据有效地精确地计算PM。在本文中,我们开发了一种在较高维度上近似PM的算法。一些数据示例表明,所提出的算法在准确性和效率方面都表现良好。作为应用程序,我们通过利用该算法的Matlab实现来研究PM的有限样本相对效率。

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