VanHuffel

The total least squares problem: computational aspects and analysis. Total least squares (TLS) is one of the several linear parameter estimation techniques that have been devised to compensate for data errors. It is also known as the errors-in-variables model. The renewed interest in the TLS method is mainly due to the development of computationally efficient and numerically reliable TLS algorithms. Much attention is paid in this book to the computational aspects of TLS and new algorithms are presented. .. (netlib vanhuffel) (Source: http://plato.asu.edu)


References in zbMATH (referenced in 208 articles , 1 standard article )

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  1. Hladík, Milan; Černý, Michal; Antoch, Jaromír: EIV regression with bounded errors in data: total `least squares’ with Chebyshev norm (2020)
  2. Liu, Qiaohua; Jin, Shufang; Yao, Lei; Shen, Dongmei: The revisited total least squares problems with linear equality constraint (2020)
  3. Quintana Carapia, Gustavo; Markovsky, Ivan; Pintelon, Rik; Csurcsia, Péter Zoltán; Verbeke, Dieter: Bias and covariance of the least squares estimate in a structured errors-in-variables problem (2020)
  4. Zhang, Liping; Wei, Yimin: Randomized core reduction for discrete ill-posed problem (2020)
  5. Hnětynková, Iveta; Plešinger, Martin; Žáková, Jana: On TLS formulation and core reduction for data fitting with generalized models (2019)
  6. Hnětynková, Iveta; Plešinger, Martin; Žáková, Jana: Solvability classes for core problems in matrix total least squares minimization. (2019)
  7. Xia, Yong; Wang, Longfei; Yang, Meijia: A fast algorithm for globally solving Tikhonov regularized total least squares problem (2019)
  8. Xu, Peiliang: Improving the weighted least squares estimation of parameters in errors-in-variables models (2019)
  9. Blanco, Víctor; Puerto, Justo; Salmerón, Román: Locating hyperplanes to fitting set of points: a general framework (2018)
  10. Fasino, Dario; Fazzi, Antonio: A Gauss-Newton iteration for total least squares problems (2018)
  11. Li, Hanyu; Wang, Shaoxin: On the partial condition numbers for the indefinite least squares problem (2018)
  12. Salahi, M.; Taati, A.: An efficient algorithm for solving the generalized trust region subproblem (2018)
  13. Shklyar, Sergiy: Consistency of the total least squares estimator in the linear errors-in-variables regression (2018)
  14. Yang, Meijia; Xia, Yong; Wang, Jiulin; Peng, Jiming: Efficiently solving total least squares with Tikhonov identical regularization (2018)
  15. Diao, Huai-An; Wei, Yimin; Xie, Pengpeng: Small sample statistical condition estimation for the total least squares problem (2017)
  16. Dutta, Aritra; Li, Xin: On a problem of weighted low-rank approximation of matrices (2017)
  17. Goos, Jan; Lataire, John; Louarroudi, Ebrahim; Pintelon, Rik: Frequency domain weighted nonlinear least squares estimation of parameter-varying differential equations (2017)
  18. Hidayat, Z.; Babuška, R.; Núñez, A.; De Schutter, B.: Identification of distributed-parameter systems from sparse measurements (2017)
  19. Volkov, V. V.; Erokhin, V. I.; Krasnikov, A. S.; Razumov, A. V.; Khvostov, M. N.: Minimum-Euclidean-norm matrix correction for a pair of dual linear programming problems (2017)
  20. Xie, Pengpeng; Xiang, Hua; Wei, Yimin: A contribution to perturbation analysis for total least squares problems (2017)

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