Accession Number : AD1014734

Title :   Statistical Inference on Memory Structure of Processes and Its Applications to Information Theory

Descriptive Note : Technical Report

Corporate Author : University of Kansas Lawrence United States

Personal Author(s) : Talata,Zsolt

Full Text :

Report Date : 12 May 2016

Pagination or Media Count : 61

Abstract : 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.)

Descriptors :   STATISTICAL ANALYSIS , MATHEMATICAL MODELS , RANDOM VARIABLES , INFORMATION THEORY , probability , markov chains , algorithms , ergodic processes , polynomials , inequalities , TIME SERIES ANALYSIS

Distribution Statement : APPROVED FOR PUBLIC RELEASE