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- W190858178 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 $$ {y_i} = fleft( {{x_i} + {delta _i},beta } right) + {varepsilon _i},left( {i = 1, ldots ,n} right) $$(1)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 e 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." @default.
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- W190858178 date "1998-01-01" @default.
- W190858178 modified "2023-09-24" @default.
- W190858178 title "Algorithms for Robustified Error-in-Variables Problems" @default.
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- W190858178 doi "https://doi.org/10.1007/978-3-662-01131-7_37" @default.
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