Robust regression using iteratively reweighted least-squares

作者：Paul W. Holland, Roy E. Welsch · 发表于：Communication in Statistics- Theory and Methods · 年份：1977 · DOI：10.1080/03610927708827533 · 被引用次数：2140 · 研究领域：Advanced Statistical Methods and Models、Advanced Statistical Process Monitoring、Statistical and numerical algorithms

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 (RObust Statistical Estimation PACKage) 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.