A unified approach to the moments based distribution estimation - Unbounded support

Árpád Tari, Miklós Telek, Peter Buchholz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

The problem of moments has been studied for more than a century. This paper discusses a practical issue related to the problem of moments namely the bounding of a distribution based on a given number of moments. The presented approach is unified in the sense that all measures of interests are provided as a quadratic expression of the same Hankel-matrix. Application examples indicate the importance of the presented approach.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages79-93
Number of pages15
DOIs
Publication statusPublished - Dec 1 2005
EventEuropean Performance Engineering Workshop, EPEW 2005 and International Workshop on Web Services and Formal Methods, WS-FM 2005 - Versailles, France
Duration: Sep 1 2005Sep 3 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3670 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Performance Engineering Workshop, EPEW 2005 and International Workshop on Web Services and Formal Methods, WS-FM 2005
CountryFrance
CityVersailles
Period9/1/059/3/05

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Keywords

  • Hankel matrix
  • Moments based distribution bounding
  • Reduced moment problem

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Tari, Á., Telek, M., & Buchholz, P. (2005). A unified approach to the moments based distribution estimation - Unbounded support. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 79-93). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3670 LNCS). https://doi.org/10.1007/11549970_7