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Statements

Subject Item
n2:RIV%2F68407700%3A21340%2F11%3A00189482%21RIV12-MSM-21340___
rdf:type
n9:Vysledek skos:Concept
dcterms:description
Classical regression estimators, such as the Ordinary Least Squares, are very sensitive to occurrence of outliers and they are not consistent when orthogonality condition is broken and both independent and some dependent variables are considered to be measured with a random error. The method which can cope with this problem is robustified version of the mixed Least Squares - Total Least Squares method, based on the idea of down-weighting the influential points. The existence and the uniqueness of the solution is discussed and di(R)erent approaches of calculation are described. The computational complexity is shown and the exact algorithm based on a branch-and-bound technique that guarantees global optimality is presented. The implementation of the algorithms and theirs application to simulated and real data sets are shown. Classical regression estimators, such as the Ordinary Least Squares, are very sensitive to occurrence of outliers and they are not consistent when orthogonality condition is broken and both independent and some dependent variables are considered to be measured with a random error. The method which can cope with this problem is robustified version of the mixed Least Squares - Total Least Squares method, based on the idea of down-weighting the influential points. The existence and the uniqueness of the solution is discussed and di(R)erent approaches of calculation are described. The computational complexity is shown and the exact algorithm based on a branch-and-bound technique that guarantees global optimality is presented. The implementation of the algorithms and theirs application to simulated and real data sets are shown.
dcterms:title
Some computational aspects of robustifed total least squares Some computational aspects of robustifed total least squares
skos:prefLabel
Some computational aspects of robustifed total least squares Some computational aspects of robustifed total least squares
skos:notation
RIV/68407700:21340/11:00189482!RIV12-MSM-21340___
n9:predkladatel
n21:orjk%3A21340
n5:aktivita
n8:S
n5:aktivity
S
n5:dodaniDat
n6:2012
n5:domaciTvurceVysledku
n19:8465568
n5:druhVysledku
n17:D
n5:duvernostUdaju
n14:S
n5:entitaPredkladatele
n7:predkladatel
n5:idSjednocenehoVysledku
230603
n5:idVysledku
RIV/68407700:21340/11:00189482
n5:jazykVysledku
n16:eng
n5:klicovaSlova
regression coeficients, least square method
n5:klicoveSlovo
n12:least%20square%20method n12:regression%20coeficients
n5:kontrolniKodProRIV
[83E00F5FFCFF]
n5:mistoKonaniAkce
Křižánky
n5:mistoVydani
Praha
n5:nazevZdroje
SPMS 2011 Stochastic and Physical Monitoring Systems - Proceedings
n5:obor
n20:BA
n5:pocetDomacichTvurcuVysledku
1
n5:pocetTvurcuVysledku
1
n5:rokUplatneniVysledku
n6:2011
n5:tvurceVysledku
Franc, Jiří
n5:typAkce
n18:EUR
n5:zahajeniAkce
2011-06-27+02:00
s:numberOfPages
13
n13:hasPublisher
České vysoké učení technické v Praze. Fakulta jaderná a fyzikálně inženýrská
n15:isbn
978-80-01-04916-7
n4:organizacniJednotka
21340