Statistical Inference on Memory Structure of Processes and Its Applications to Information Theory
University of Kansas Lawrence United States
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Three areas were investigated. First, new memory models of discrete-time and finitely-valued information sources are introduced and a universal code for the new model class is presented. An algorithm is developed to compute the code, and its practical polynomial computational and storage complexities are proved. Second, a statistical method is developed to estimate the memory depth of discrete-time and continuously-valued times series from a sample. A practical algorithm to compute the estimator is a work in progress.