Asymptotic distributions

Abstract

This chapter focuses on the asymptotic distribution of the parameter estimates for the case when the number of measured data grows without bound. In general terms, the parameter estimates are then asymptotically Gaussian distributed. It is shown for all the main methods treated in the book, what the covariance matrix of that Gaussian distribution looks like. It thus gives a measure of the accuracy of the obtained parameter estimates. The chapter includes also some results on Cramér–Rao lower bounds (CRLB) on this covariance matrix. Explicit algorithms to compute the covariance matrix for different estimation methods and for the CRLB are given.