Robust Estimation When More Than One Variable
نویسنده
چکیده
In a least squares adjustment when more than one variable in an equation of condition has error, the results will be affected by unnecessary asymptotic bias unless the algorithm is properly formulated. Similar difficulties can be expected with robust estimation techniques that are based on extending least squares to a noneuclidean metric. This paper presents an algorithm for robust estimation in the case that each equation of condition contains several observations. The properties of the estimates produced by the algorithm are investigated, and a numerical example using real data on galaxies is given.
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