Abstract
The recently-introduced Extended Least-Squares (XLS) parameters — estimation criterion is aimed at discriminating measurement errors from modelling errors (or “misfit” from “latency” errors). Using versatile weighting of “presumed errors”, it encompasses the classical Least-Squares (LS) criterion on one hand, and the (Structured, or Constrained) Total LS [(S,C)TLS] criteria on the other hand. Thus, the STLS algorithm, originally aimed at solving TLS problems with structural constraints, can be modified, or “extended”, to solve the XLS minimization problem. In this paper we introduce the Extended STLS algorithm, and demonstrate its use in the XLS context with estimating the parameters of a noisy Auto-Regressive (AR) process. We briefly compare the Extended STLS algorithm to other algorithms serving the same purpose.
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© 2002 Springer Science+Business Media Dordrecht
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Yeredor, A. (2002). The Extended STLS Algorithm for Minimizing the Extended LS Criterion. In: Van Huffel, S., Lemmerling, P. (eds) Total Least Squares and Errors-in-Variables Modeling. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-3552-0_10
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DOI: https://doi.org/10.1007/978-94-017-3552-0_10
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