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Concentration and Tail Bounds for Missing Mass
Published in Institute of Electrical and Electronics Engineers Inc.
2019
Volume: 2019-July
   
Pages: 1862 - 1866
Abstract
The missing mass of a sequence is defined as the total probability of the elements that have not appeared or occurred in the sequence. Estimation of missing mass is an important ingredient in many practical applications in language modeling and ecology. Exponential tail bounds have been known for missing mass, and improving them results in better confidence in estimation. In this work, we improve upon the best-known left and right tail bounds for missing mass. For the left tail, our proof method is arguably simpler than prior methods and provides a better bound for small sample sizes. For the right tail, we provide a new bounding method for the moment generating function of a generalized version of missing mass that results in a noticeable improvement in the tail bound. © 2019 IEEE.
About the journal
JournalData powered by TypesetIEEE International Symposium on Information Theory - Proceedings
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
ISSN21578095
Open AccessNo
Concepts (9)
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    Modeling languages
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    BOUNDING METHOD
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    Exponential tail
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    Language model
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    MOMENT GENERATING FUNCTION
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    PROOF METHODS
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    SMALL SAMPLE SIZE
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    TOTAL PROBABILITIES
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    Information theory