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dc.contributor.authorBaryamureeba, Venansius
dc.date.accessioned2022-07-18T11:28:49Z
dc.date.available2022-07-18T11:28:49Z
dc.date.issued2001
dc.identifier.citationBaryamureeba, V. (2001). The impact of equal weighting of low-and high-confidence observations on robust linear regression computations. BIT Numerical Mathematics, 41(5), 847-855.en_US
dc.identifier.urihttps://link.springer.com/article/10.1023/A:1021912522498
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4222
dc.description.abstractEqual weighting of low- and high-confidence observations occurs for Huber, Talwar, and Barya weighting functions when Newton’s method is used to solve robust linear regression problems. This leads to easy updates and/or downdates of existing matrix factorizations or easy computation of coefficient matrices in linear systems from previous ones. Thus Newton’s method based on these functions has been shown to be computationally cheap. In this paper we show that a combination of Newton’s method and an iterative method is a promising approach for solving robust linear regression problems. We show that Newton’s method based on the Talwar function is an active set method. Further we show that it is possible to obtain improved estimates of the solution vector by combining a line search method like Newton’s method with an active set method.en_US
dc.language.isoenen_US
dc.publisherBIT Numerical Mathematicsen_US
dc.subjectRobust linear regressionen_US
dc.subjectNewton’s methoden_US
dc.subjectConjugate gradient least squares methoden_US
dc.subjectLSQRen_US
dc.subjectPreconditioneren_US
dc.subjectBasis identification techniquesen_US
dc.titleThe Impact of Equal Weighting of Low and High-Confidence Observations on Robust Linear Regression Computationsen_US
dc.typeOtheren_US


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