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Statements

Subject Item
n2:RIV%2F00216224%3A14560%2F07%3A00020673%21RIV10-GA0-14560___
rdf:type
n15:Vysledek skos:Concept
dcterms:description
In this paper we derive essential relations which are necessary for application of the principle of overcompleteness to sparse parameter estimation in multivariate ARMA models (VARMA models). This new approach is based on the Basis Pursuit Algorithm originally suggested by Chen et al [SIAM Review 43 (2001), No.1]. Overcompleteness means that we admit higher range of orders within which we are looking for lowest possible number of significant parameters (sparsity). A previous study [V. Veselý and J. Tonner: Austrian Journal of Statistics, Special Issue 2006] confirmed that this relaxation of the commonly used low-order assumption may yield more precise forecasts from ARMA models when compared with standard statistical estimation techniques. The results of the numerical simulation study and the tests on real data can be seen in [Mathematical Methods in Economics 2006, J. Tonner: The Principle of Overcompleteness in Economic Multivariate Time Series Models]. In this paper we derive essential relations which are necessary for application of the principle of overcompleteness to sparse parameter estimation in multivariate ARMA models (VARMA models). This new approach is based on the Basis Pursuit Algorithm originally suggested by Chen et al [SIAM Review 43 (2001), No.1]. Overcompleteness means that we admit higher range of orders within which we are looking for lowest possible number of significant parameters (sparsity). A previous study [V. Veselý and J. Tonner: Austrian Journal of Statistics, Special Issue 2006] confirmed that this relaxation of the commonly used low-order assumption may yield more precise forecasts from ARMA models when compared with standard statistical estimation techniques. The results of the numerical simulation study and the tests on real data can be seen in [Mathematical Methods in Economics 2006, J. Tonner: The Principle of Overcompleteness in Economic Multivariate Time Series Models].
dcterms:title
The Principle of Overcompleteness in VARMA Models The Principle of Overcompleteness in VARMA Models
skos:prefLabel
The Principle of Overcompleteness in VARMA Models The Principle of Overcompleteness in VARMA Models
skos:notation
RIV/00216224:14560/07:00020673!RIV10-GA0-14560___
n4:aktivita
n17:P
n4:aktivity
P(GA402/05/2172)
n4:dodaniDat
n14:2010
n4:domaciTvurceVysledku
n5:1347411
n4:druhVysledku
n20:D
n4:duvernostUdaju
n12:S
n4:entitaPredkladatele
n6:predkladatel
n4:idSjednocenehoVysledku
444218
n4:idVysledku
RIV/00216224:14560/07:00020673
n4:jazykVysledku
n18:eng
n4:klicovaSlova
multivariate time series; sparse system; overcomplete system; VARMA models; l1 norm optimization; stationary time series
n4:klicoveSlovo
n11:stationary%20time%20series n11:VARMA%20models n11:multivariate%20time%20series n11:l1%20norm%20optimization n11:sparse%20system n11:overcomplete%20system
n4:kontrolniKodProRIV
[09C22202F62B]
n4:mistoKonaniAkce
Brno
n4:mistoVydani
Brno
n4:nazevZdroje
Summer School DATASTAT 06, Proceedings, Masaryk Univeristy, 2007
n4:obor
n19:AH
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
1
n4:projekt
n13:GA402%2F05%2F2172
n4:rokUplatneniVysledku
n14:2007
n4:tvurceVysledku
Tonner, Jaromír
n4:typAkce
n7:CST
n4:zahajeniAkce
2006-01-01+01:00
s:numberOfPages
4
n10:hasPublisher
Masaryk University
n16:isbn
978-80-210-4493-7
n9:organizacniJednotka
14560