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dc.contributor.authorGolub, Gene H.en_US
dc.contributor.authorVan Loan, Charlesen_US
dc.date.accessioned2007-04-23T16:38:38Z
dc.date.available2007-04-23T16:38:38Z
dc.date.issued1980-02en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR80-411en_US
dc.identifier.urihttps://hdl.handle.net/1813/6251
dc.description.abstractTotla least squares (TLS) is a method of fitting that is appropriate when there are errors in both the observation vector $b (mxl)$ and in the data matrix $A (mxn)$. The technique has been discussed by several authors and amounts to fitting a "best" subspace to the points $(a^{T}_{i},b_{i}), i=1,\ldots,m,$ where $a^{T}_{i}$ is the $i$-th row of $A$. In this paper a singular value decomposition analysis of the TLS problem is presented. The sensitivity of the TLS problem as well as its relationship to ordinary least squares regression is explored. Aan algorithm for solving the TLS problem is proposed that utilizes the singular value decomposition and which provides a measure of the underlying problem's sensitivity.en_US
dc.format.extent1026390 bytes
dc.format.extent318433 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleAn Analysis of the Total Least Squares Problemen_US
dc.typetechnical reporten_US


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