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 l2 identification, but lacks a corresponding property in l1 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 lp identification methods
Godkänd; 1995; 20100913 (andbra)