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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F13%3A10172753%21RIV14-MSM-11320___
rdf:type
skos:Concept n13:Vysledek
dcterms:description
Every process in our environment can be described with a statistical model containing inner properties expressed by parameters. These are usually unknown and the determination of their values is of interest in the statistical branch called parameter estimation. This branch involves many methods solving different estimation cases, e.g. the estimation of location and scale parameters. To obtain the parameter estimate we exploit the data given by data sources. In particular, the estimate is their combination. Improvement of the parameter estimates involve the assignment of the weights to the data sources resulting in a weighted combination of data. In this paper we focus on the derivation of the weights arisen within the Supra-Bayesian approach and on the simulation study of their behaviour and the behaviour of the final estimate. Every process in our environment can be described with a statistical model containing inner properties expressed by parameters. These are usually unknown and the determination of their values is of interest in the statistical branch called parameter estimation. This branch involves many methods solving different estimation cases, e.g. the estimation of location and scale parameters. To obtain the parameter estimate we exploit the data given by data sources. In particular, the estimate is their combination. Improvement of the parameter estimates involve the assignment of the weights to the data sources resulting in a weighted combination of data. In this paper we focus on the derivation of the weights arisen within the Supra-Bayesian approach and on the simulation study of their behaviour and the behaviour of the final estimate.
dcterms:title
On Supra-Bayesian weighted combination of available data determined by Kerridge inaccuracy and entropy On Supra-Bayesian weighted combination of available data determined by Kerridge inaccuracy and entropy
skos:prefLabel
On Supra-Bayesian weighted combination of available data determined by Kerridge inaccuracy and entropy On Supra-Bayesian weighted combination of available data determined by Kerridge inaccuracy and entropy
skos:notation
RIV/00216208:11320/13:10172753!RIV14-MSM-11320___
n13:predkladatel
n14:orjk%3A11320
n3:aktivita
n9:S
n3:aktivity
S
n3:cisloPeriodika
May
n3:dodaniDat
n8:2014
n3:domaciTvurceVysledku
n18:2844737
n3:druhVysledku
n17:J
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
93825
n3:idVysledku
RIV/00216208:11320/13:10172753
n3:jazykVysledku
n11:eng
n3:klicovaSlova
parameter estimation; maximum entropy principle; Kerridge inaccuracy
n3:klicoveSlovo
n5:parameter%20estimation n5:maximum%20entropy%20principle n5:Kerridge%20inaccuracy
n3:kodStatuVydavatele
BG - Bulharská republika
n3:kontrolniKodProRIV
[ABC48B9F4E2D]
n3:nazevZdroje
Pliska Studia Mathematica Bulgarica
n3:obor
n4:BD
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:rokUplatneniVysledku
n8:2013
n3:svazekPeriodika
22
n3:tvurceVysledku
Sečkárová, Vladimíra
s:issn
0204-9805
s:numberOfPages
10
n16:organizacniJednotka
11320