Abstract
We consider worst-case analysis of system identification under less restrictive assumptions on the noise than the l ∞ bounded error condition. It is shown that the least-squares method has a robust convergence property in l 2 identification, but lacks a corresponding property in l 1 identification (as well as in all other non-Hilbert space settings). The latter result is in stark contrast with typical results in asymptotic stochastic analysis of the least-squares method. Furthermore, it is shown that the Khintchine inequality is useful in the analysis of least l p identification methods.
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