Title
Minimax Pointwise Redundancy for Memoryless Models Over Large Alphabets
Abstract
We study the minimax pointwise redundancy of universal coding for memoryless models over large alphabets and present two main results. We first complete studies initiated in Orlitsky and Santhanam deriving precise asymptotics of the minimax pointwise redundancy for all ranges of the alphabet size relative to the sequence length. Second, we consider the minimax pointwise redundancy for a family of models in which some symbol probabilities are fixed. The latter problem leads to a binomial sum for functions with superpolynomial growth. Our findings can be used to approximate numerically the minimax pointwise redundancy for various ranges of the sequence length and the alphabet size. These results are obtained by analytic techniques such as tree-like generating functions and the saddle point method.
Year
DOI
Venue
2012
10.1109/TIT.2012.2195769
IEEE Transactions on Information Theory
Keywords
Field
DocType
data model,computational modeling,encoding,probability distribution,data models,probability,source coding,computer model,redundancy,generating function
Generating function,Discrete mathematics,Combinatorics,Minimax,Saddle point,Redundancy (engineering),Probability distribution,Asymptotic analysis,Mathematics,Encoding (memory),Pointwise
Journal
Volume
Issue
ISSN
58
7
0018-9448
Citations 
PageRank 
References 
7
0.50
13
Authors
2
Name
Order
Citations
PageRank
Wojciech Szpankowski11557192.33
Marcelo J. Weinberger217225.18