Competitive Signal Processing

142 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.Finally, we conclude the thesis by investigating the competitive prediction problem in a probabilistic setting. Here we investigate a particular algorithm and show that this algorithm is universal such that it asymptotically achieves the performance of the best predictor for Gaussian AR sources with unknown order up to some maximal order M.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD