Title :
Nonparametric maximum entropy
Author :
Politis, Dimitris Nicolas
Author_Institution :
Dept. of Stat., Purdue Univ., West Lafayette, IN, USA
fDate :
7/1/1993 12:00:00 AM
Abstract :
The standard maximum entropy method developed by J.P. Burg (1967) and the resulting autoregressive model have been widely applied to spectrum estimation and prediction. A generalization of the maximum entropy formalism in a nonparametric setting is presented, and the class of the resulting solutions is identified to be a class of Markov processes. The proof is based on a string of information theoretic arguments developed in a derivation of Burg´s maximum entropy spectrum by B.S. Choi and T.M. Cover (1984). A framework for the practical implementation of the proposed method is presented in the context of both continuous and discrete data
Keywords :
Markov processes; entropy; filtering and prediction theory; information theory; nonparametric statistics; Markov processes; autoregressive model; continuous data; discrete data; information theoretic arguments; nonparametric maximum entropy; spectrum estimation; spectrum prediction; Binary sequences; Density measurement; Entropy; Extrapolation; Gaussian processes; Markov processes; Predictive models; Random variables; Statistical distributions; Stochastic processes;
Journal_Title :
Information Theory, IEEE Transactions on
