Abstract
We consider the problem of fitting a model of the form y = f (x, β) to a set of points (x i , y i ), i = 1,..., n. If there are measurement or observation errors in x as well as in y, we have the so called errors-in-variables-problem with model equation
where δ i ∈ ℝm, i = 1,..., n are the errors in x i ∈ ℝm. Then the problem is to find a vector of parameters β ∈ ℝ p that minimizes the errors ε i and δ i in some loss function subject to (1). We will present algorithms using more robust alternatives to the least squares criterion. Figure 1 gives examples where the least squares (L2), the least absolute deviation (L1) and the Huber criteria are used.
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© 1998 Springer-Verlag Berlin Heidelberg
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Ekblom, H., Edlund, O. (1998). Algorithms for Robustified Error-in-Variables Problems. In: Payne, R., Green, P. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-01131-7_37
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DOI: https://doi.org/10.1007/978-3-662-01131-7_37
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1131-5
Online ISBN: 978-3-662-01131-7
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