TY - JOUR

T1 - The extended least squares criterion

T2 - Minimization algorithms and applications

AU - Yeredor, Arie

PY - 1999

Y1 - 1999

N2 - The least squares (LS) estimation criterion on one hand, and the total LS (TLS), constrained TLS (CTLS), and structured TLS (STLS) criteria, on the other hand, can be viewed as opposite limiting cases of a more general criterion, which we term "extended LS" (XLS). The XLS criterion distinguishes measurement errors from modeling errors by properly weighting and balancing the two error sources. In the context of certain models (termed "pseudo-linear"), we derive two iterative algorithms for minimizing the XLS criterion: One is a straightforward "alternating coordinates" minimization, and the other is an extension of an existing CTLS algorithm. The algorithms exhibit different tradeoffs between convergence rate, computational load, and accuracy. The XLS criterion can be applied to popular estimation problems, such as identifying an autoregressive (AR) with erogenous noise (ARX) system from noisy input/output measurements or estimating the parameters of an AR process from noisy measurements. We demonstrate the convergence properties and performance of the algorithms with an example of the latter.

AB - The least squares (LS) estimation criterion on one hand, and the total LS (TLS), constrained TLS (CTLS), and structured TLS (STLS) criteria, on the other hand, can be viewed as opposite limiting cases of a more general criterion, which we term "extended LS" (XLS). The XLS criterion distinguishes measurement errors from modeling errors by properly weighting and balancing the two error sources. In the context of certain models (termed "pseudo-linear"), we derive two iterative algorithms for minimizing the XLS criterion: One is a straightforward "alternating coordinates" minimization, and the other is an extension of an existing CTLS algorithm. The algorithms exhibit different tradeoffs between convergence rate, computational load, and accuracy. The XLS criterion can be applied to popular estimation problems, such as identifying an autoregressive (AR) with erogenous noise (ARX) system from noisy input/output measurements or estimating the parameters of an AR process from noisy measurements. We demonstrate the convergence properties and performance of the algorithms with an example of the latter.

UR - http://www.scopus.com/inward/record.url?scp=33747673624&partnerID=8YFLogxK

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AN - SCOPUS:33747673624

SN - 1053-587X

VL - 47

SP - 2901

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 10

ER -