Divergence measures and weak majorization in estimation problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F14%3A86091093" target="_blank" >RIV/61989100:27510/14:86091093 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Divergence measures and weak majorization in estimation problems

Popis výsledku v původním jazyce

Statistical inference can be interpreted as a problem of minimum distance between an empirical (observed) and a theoretical distribution. The most used measures of dissimilarity/disparity between probability distributions are the well known divergence measures. These measures are not symmetric: basing on the duality in their formulation, we classify divergences within the context of estimation into two main classes and analyze them with reference to majorization theory. In this regard, the consistency of divergence measures with respect to the generalized (strong) majorization pre-order is can be easily derived from a well known characterization theorem. Nevertheless, in many practical contexts such as estimation problem, one of the main assumption for(strong) majorization could be unfulfilled. Thus we study under which conditions divergence measures are consistent with respect to the generalization of weak majorization (from above). This paper provides a guideline for the choice of a

Název v anglickém jazyce

Divergence measures and weak majorization in estimation problems

Popis výsledku anglicky

Statistical inference can be interpreted as a problem of minimum distance between an empirical (observed) and a theoretical distribution. The most used measures of dissimilarity/disparity between probability distributions are the well known divergence measures. These measures are not symmetric: basing on the duality in their formulation, we classify divergences within the context of estimation into two main classes and analyze them with reference to majorization theory. In this regard, the consistency of divergence measures with respect to the generalized (strong) majorization pre-order is can be easily derived from a well known characterization theorem. Nevertheless, in many practical contexts such as estimation problem, one of the main assumption for(strong) majorization could be unfulfilled. Thus we study under which conditions divergence measures are consistent with respect to the generalization of weak majorization (from above). This paper provides a guideline for the choice of a

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0016" target="_blank" >EE2.3.30.0016: Příležitost pro mladé výzkumníky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2nd International Conference on Mathematical, Computational and Statistical Sciences (MCSS '14); Proceedings of the 7th International Conference on Finite Difference...: Gdansk, Poland May 15-17, 2014

ISBN

978-960-474-380-3

ISSN

2227-4588

e-ISSN

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Počet stran výsledku

6

Strana od-do

152-157

Název nakladatele

WSEAS Press

Místo vydání

Cambridge

Místo konání akce

Gdaňsk

Datum konání akce

25. 5. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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