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Statements

Subject Item
n2:RIV%2F00216275%3A25530%2F14%3A39899012%21RIV15-MSM-25530___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
The aim in linear statistical models is to determine an estimator of the unknown parameters on the basis of the observation vector. One possible approach used mainly in geodetic measurements is known as H-optimum estimator. This paper deals with problem of connecting measurements where boundaries of estimators dispersion are previously known. The H-optimum estimators seem to be appropriate for reducing the influence of B-type metrological uncertainty on the estimator in connecting measurement. However in this case, general H-optimum estimators do not solve the problem of bounded dispersion completely. Heuristic methods such as algorithm complex method help us to extend H-optimum estimator theory so given dispersion boundaries could be satisfied. Presented paper describes standard theory of H-optimum estimators and its extension with heuristics utilization. Finally, qualities of extended H-optimum estimator are shown by solving illustration example. The aim in linear statistical models is to determine an estimator of the unknown parameters on the basis of the observation vector. One possible approach used mainly in geodetic measurements is known as H-optimum estimator. This paper deals with problem of connecting measurements where boundaries of estimators dispersion are previously known. The H-optimum estimators seem to be appropriate for reducing the influence of B-type metrological uncertainty on the estimator in connecting measurement. However in this case, general H-optimum estimators do not solve the problem of bounded dispersion completely. Heuristic methods such as algorithm complex method help us to extend H-optimum estimator theory so given dispersion boundaries could be satisfied. Presented paper describes standard theory of H-optimum estimators and its extension with heuristics utilization. Finally, qualities of extended H-optimum estimator are shown by solving illustration example.
dcterms:title
Heuristics and H-optimum Estimators in a Model with Type-I Constraints Heuristics and H-optimum Estimators in a Model with Type-I Constraints
skos:prefLabel
Heuristics and H-optimum Estimators in a Model with Type-I Constraints Heuristics and H-optimum Estimators in a Model with Type-I Constraints
skos:notation
RIV/00216275:25530/14:39899012!RIV15-MSM-25530___
n3:aktivita
n4:I n4:P
n3:aktivity
I, P(EE2.3.30.0058)
n3:dodaniDat
n14:2015
n3:domaciTvurceVysledku
n15:1528424 n15:3487520
n3:druhVysledku
n10:D
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
19007
n3:idVysledku
RIV/00216275:25530/14:39899012
n3:jazykVysledku
n9:eng
n3:klicovaSlova
Algorithm complex method, linear statistical model, H-optimum estimators, BLUE, uncertainty of types A and B, covariance matrix, problem of bounds for dispersion of estimators
n3:klicoveSlovo
n6:BLUE n6:uncertainty%20of%20types%20A%20and%20B n6:Algorithm%20complex%20method n6:problem%20of%20bounds%20for%20dispersion%20of%20estimators n6:H-optimum%20estimators n6:covariance%20matrix n6:linear%20statistical%20model
n3:kontrolniKodProRIV
[5C080C5EC3AF]
n3:mistoKonaniAkce
Ostrava
n3:mistoVydani
New York
n3:nazevZdroje
Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014
n3:obor
n7:BB
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n16:EE2.3.30.0058
n3:rokUplatneniVysledku
n14:2014
n3:tvurceVysledku
Marek, Jaroslav Heckenbergerová, Jana
n3:typAkce
n8:WRD
n3:zahajeniAkce
2014-06-23+02:00
s:numberOfPages
10
n11:hasPublisher
Springer-Verlag
n20:isbn
978-3-319-08155-7
n12:organizacniJednotka
25530