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Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Imprimer: 1064-2315
ISSN En ligne: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v41.i5.50
pages 41-51

Asymptotics of Linear Recurrent Regression under Diffuse Initialization

Boris A. Skorohod
Sevastopol National Technical University, Ukraine

RÉSUMÉ

The behavior of recurrent least squares method in the absence of a priori information with respect to estimated unknown parameters is studied. The developed approach allows one to present its characteristics in analytical form, to explain the divergence phenomenon and to suggest a limiting recurrent estimation algorithm independent of a large parameter characterizing initial uncertainty and leading to divergence.


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