ABSTRACT
Preceding chapters have highlighted the need for estimating the signal properties and also discussed in detail the methods to obtain the same. Estimation of the properties of signals, be it output or input, is important at different stages of identification. In Chapter 2 we observed that the first step towards building a (linear) data-driven model is the construction of deviation quantities. A generalization of this step is the removal of trends. In all estimation exercises, an important post-estimation step is the computation of standard errors, which requires the know-how of computing variance of observation noise. The Fisher’s information metric gives us the extent of information available in a data given a theoretical model. In practice, we need an empirical version of the same. On the other side of the story is the need to compute spectral densities for the estimation of frequency response functions and input-output delays.
