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Statements

Subject Item
n2:RIV%2F00216224%3A14560%2F13%3A00071669%21RIV14-MSM-14560___
rdf:type
n6:Vysledek skos:Concept
rdfs:seeAlso
http://www.mii.lt/na/issues/NA_1802/NA18205.pdf
dcterms:description
The contribution is focused on detection of multiple changes in the mean in a onedimensional stochastic process by sparse parameter estimation from an overparametrized model. The authors’ approach to change point detection differs entirely from standard statistical techniques. A stochastic process residing in a bounded interval with changes in the mean is estimated using dictionary (a family of functions, the so-called atoms, which are overcomplete in the sense of being nearly linearly dependent) and consisting of Heaviside functions. Among all possible representations of the process we want to find a sparse one utilizing a significantly reduced number of atoms. This problem can be solved by \ell_1-minimization. The basis pursuit algorithm is used to get sparse parameter estimates. The contribution is focused on detection of multiple changes in the mean in a onedimensional stochastic process by sparse parameter estimation from an overparametrized model. The authors’ approach to change point detection differs entirely from standard statistical techniques. A stochastic process residing in a bounded interval with changes in the mean is estimated using dictionary (a family of functions, the so-called atoms, which are overcomplete in the sense of being nearly linearly dependent) and consisting of Heaviside functions. Among all possible representations of the process we want to find a sparse one utilizing a significantly reduced number of atoms. This problem can be solved by \ell_1-minimization. The basis pursuit algorithm is used to get sparse parameter estimates.
dcterms:title
Detection of Multiple Changes in Mean by Sparse Parameter Estimation Detection of Multiple Changes in Mean by Sparse Parameter Estimation
skos:prefLabel
Detection of Multiple Changes in Mean by Sparse Parameter Estimation Detection of Multiple Changes in Mean by Sparse Parameter Estimation
skos:notation
RIV/00216224:14560/13:00071669!RIV14-MSM-14560___
n6:predkladatel
n19:orjk%3A14560
n3:aktivita
n13:P n13:N
n3:aktivity
N, P(GPP402/10/P209)
n3:cisloPeriodika
2
n3:dodaniDat
n15:2014
n3:domaciTvurceVysledku
n20:5758343
n3:druhVysledku
n8:J
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n12:predkladatel
n3:idSjednocenehoVysledku
68737
n3:idVysledku
RIV/00216224:14560/13:00071669
n3:jazykVysledku
n16:eng
n3:klicovaSlova
multiple change point detection; sparse parameter estimation; basis pursuit denoising; LASSO; \ell_1 trend filtering
n3:klicoveSlovo
n10:%5Cell_1%20trend%20filtering n10:multiple%20change%20point%20detection n10:LASSO n10:basis%20pursuit%20denoising n10:sparse%20parameter%20estimation
n3:kodStatuVydavatele
LT - Litevská republika
n3:kontrolniKodProRIV
[3C520A4B4514]
n3:nazevZdroje
Nonlinear Analysis: Modelling and Control
n3:obor
n14:BB
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
2
n3:projekt
n18:GPP402%2F10%2FP209
n3:rokUplatneniVysledku
n15:2013
n3:svazekPeriodika
18
n3:tvurceVysledku
Neubauer, Jiří Veselý, Vítězslav
n3:wos
000321935700005
s:issn
1392-5113
s:numberOfPages
14
n9:organizacniJednotka
14560