Abstract
So far, we have considered the least squares solution to a particularly simple es- 3 timation problem in a single unknown parameter. A more general problem is the estimation of the n unknown parameters aj , j = 1, 2, . . . ,n, appearing in a general nth order linear regression relationship of the form, \( x(k)={a_1}{x_1}(k)+{a_2}{x_2}(k) +\cdots +{a_n}{x_n}(k)\)
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© 2011 Springer-Verlag Berlin Heidelberg
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Young, P.C. (2011). Recursive Least Squares Estimation. In: Recursive Estimation and Time-Series Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21981-8_3
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DOI: https://doi.org/10.1007/978-3-642-21981-8_3
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21980-1
Online ISBN: 978-3-642-21981-8
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