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TLS and Its Improvements by Semiparametric Approach

In: Total Least Squares and Errors-in-Variables Modeling

Author

Listed:
  • Shun-ichi Amari

    (RIKEN Brain Science Institute)

  • Motoaki Kawanabe

    (Fraunhofer FIRST, The IDA group)

Abstract

The total least squares method seems to give a good consistent estimator in the linear error-in-variables model. However, this is not the optimal one. We give a simple adaptive method of improving the TLS estimator. The theory is based on information geometry of semiparametric statistical models, and is applicable to many other problems. Its intuitive introduction is also given.

Suggested Citation

  • Shun-ichi Amari & Motoaki Kawanabe, 2002. "TLS and Its Improvements by Semiparametric Approach," Springer Books, in: Sabine Van Huffel & Philippe Lemmerling (ed.), Total Least Squares and Errors-in-Variables Modeling, pages 15-24, Springer.
    • Handle: RePEc:spr:sprchp:978-94-017-3552-0_2
    DOI: 10.1007/978-94-017-3552-0_2
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