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Statements

Subject Item
n2:RIV%2F00216275%3A25410%2F07%3A00005479%21RIV08-MSM-25410___
rdf:type
skos:Concept n17:Vysledek
dcterms:description
Many probabilistic models are used to analyse the time series. Two different methods for bootstrapping time series are described in the paper. The simplest model ? a first order autoregressive scheme is described and presented in the practical example. The principle of this method is fitting a model and then sampling from the residuals. The different presented method is called the moving block bootstrap. An important advantage of the method is that is less ?model dependent? than the bootstrapping of residuals approach. The method is dependent on the model that is fit to the original time series. Results of both methods are compared in the paper. The methods for bootstrapping analogous to the solution for the regression models can be used and they are mentioned in the end. Many probabilistic models are used to analyse the time series. Two different methods for bootstrapping time series are described in the paper. The simplest model ? a first order autoregressive scheme is described and presented in the practical example. The principle of this method is fitting a model and then sampling from the residuals. The different presented method is called the moving block bootstrap. An important advantage of the method is that is less ?model dependent? than the bootstrapping of residuals approach. The method is dependent on the model that is fit to the original time series. Results of both methods are compared in the paper. The methods for bootstrapping analogous to the solution for the regression models can be used and they are mentioned in the end. V článku jsou popsány dvě metody analýzy časových řad bootstrapovou metodou,autoregresní model prvního řádu a model klouzavých bloků.Obě metody jsou demonstrovány i na praktických příkladech.Výsledky obou metod jsou v závěru článku porovnány.
dcterms:title
RE-SAMPLING ESTIMATION IN TIME SERIES RE-SAMPLING ESTIMATION IN TIME SERIES RE-SAMPLING ESTIMATION IN TIME SERIES
skos:prefLabel
RE-SAMPLING ESTIMATION IN TIME SERIES RE-SAMPLING ESTIMATION IN TIME SERIES RE-SAMPLING ESTIMATION IN TIME SERIES
skos:notation
RIV/00216275:25410/07:00005479!RIV08-MSM-25410___
n4:strany
145
n4:aktivita
n20:S
n4:aktivity
S
n4:dodaniDat
n9:2008
n4:domaciTvurceVysledku
n6:4031105 n6:1657445
n4:druhVysledku
n11:D
n4:duvernostUdaju
n13:S
n4:entitaPredkladatele
n15:predkladatel
n4:idSjednocenehoVysledku
447461
n4:idVysledku
RIV/00216275:25410/07:00005479
n4:jazykVysledku
n7:eng
n4:klicovaSlova
time series; first order autoregressive scheme; moving block bootstrap
n4:klicoveSlovo
n8:first%20order%20autoregressive%20scheme n8:moving%20block%20bootstrap n8:time%20series
n4:kontrolniKodProRIV
[D6462223B7A7]
n4:mistoKonaniAkce
Ponta Delgada
n4:mistoVydani
Azores, Portugal
n4:nazevZdroje
ISBIS 2007
n4:obor
n18:BB
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:rokUplatneniVysledku
n9:2007
n4:tvurceVysledku
Linda, Bohdan Kubanová, Jana
n4:typAkce
n12:WRD
n4:zahajeniAkce
2007-08-18+02:00
s:numberOfPages
1
n10:hasPublisher
University of Azores
n14:isbn
978-989-95489-0-9
n16:organizacniJednotka
25410