ROBUST REGRESSION USING ITERATIVELY RE-WEIGHTED LEAST-SQUARES
ROBUST REGRESSION USING ITERATIVELY RE-WEIGHTED LEAST-SQUARES
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DOI：
10.1080/03610927708827533
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发表时间：
1977-01-01
期刊：
影响因子：
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通讯作者：
WELSCH, RE
中科院分区：
文献类型：
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作者：
HOLLAND, PW;WELSCH, RE
稳健估计理论的快速发展（Huber, 1973）产生了产生稳健估计的计算程序的需求。我们将回顾一些不同的鲁棒线性回归的计算方法，但重点是一次迭代加权最小二乘（IRLS）。我们讨论的权重函数是名为ROSEPACK （RObustStatisticalEstimationPACKage）的半便携式子程序库的一部分，该子程序库是由作者和位于马萨诸塞州剑桥市的国家经济研究局计算机研究中心的Virginia Klema开发的。在美国国家科学基金会的支持下这个库（Klema, 1976）使得实现IRLS回归包相对简单。
The rapid development of the theory of robust estimation (Huber, 1973) has created a need for computational procedures to produce robust estimates. We will review a number of different computational approaches for robust linear regression but focus on one—iteratively reweighted least-squares (IRLS). The weight functions that we discuss are a part of a semi-portable subroutine library called ROSEPACK (RObustStatisticalEstimationPACKage) that has been developed by the authors and Virginia Klema at the Computer Research Center of the National Bureau of Economic Research, Inc. in Cambridge, Mass. with the support of the National Science Foundation. This library (Klema, 1976) makes it relatively simple to implement an IRLS regression package.