Accession Number : ADA290694
Title : Optimal Universal Coding and Density Estimation.
Descriptive Note : Final rept. 1 Jun 91-30 Sep 94,
Corporate Author : WISCONSIN UNIV-MADISON DEPT OF STATISTICS
Personal Author(s) : Yu, Bin
PDF Url : ADA290694
Report Date : 28 NOV 1994
Pagination or Media Count : 5
Abstract : Research progress has been made in the areas of empirical processes for mixing sequences, information theory, minimax estimation theory in source coding and nonparametric statistics, and Markov chain Monte Carlo (MCMC) methods. Rates of convergence and Central Limit Theorems results have been obtained for empirical processes of dependent data, and they are very useful for studying statistical models with dependence structure. On the important MCMC convergence diagnostic problem, regeneration points have been introduced into the Markov chain using the split-chain technique; so has been a global approach based on the the estimated L1 error and the Cusum path plot. Making connections between information theory and statistics, we obtained an information-theoretic result on the rate of convergence of a D-semifaithful code, and we also introduced non-parametric minimax lower bound techniques into bounding from below the redundancy in source coding. (AN)
Descriptors : *MONTE CARLO METHOD, *MARKOV PROCESSES, MATHEMATICAL MODELS, COMPUTATIONS, PROBABILITY DENSITY FUNCTIONS, ESTIMATES, NONPARAMETRIC STATISTICS, CODING, CONVERGENCE, BAYES THEOREM, INFORMATION THEORY, SEQUENCES(MATHEMATICS).
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE