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Statements

Subject Item
n2:RIV%2F68407700%3A21340%2F11%3A00187457%21RIV12-MSM-21340___
rdf:type
n6:Vysledek skos:Concept
rdfs:seeAlso
http://dx.doi.org/10.1007/978-3-642-20853-9_21
dcterms:description
A Fay-Herriot model having both fixed and random effects is introduced to estimate linear parameters of small areas. The model is applicable to data having a small subset of domains where direct estimates of the variable of interest cannot be described in the same way as in its complementary subset of domains. Algorithms and formulas to fit the model, to calculate EBLUPs and to estimate mean squared errors are given. A Monte Carlo simulation experiment is carried out to investigate the gain of precision obtained by using the proposed model. An application to Spanish Labour Force Survey data is also given. A Fay-Herriot model having both fixed and random effects is introduced to estimate linear parameters of small areas. The model is applicable to data having a small subset of domains where direct estimates of the variable of interest cannot be described in the same way as in its complementary subset of domains. Algorithms and formulas to fit the model, to calculate EBLUPs and to estimate mean squared errors are given. A Monte Carlo simulation experiment is carried out to investigate the gain of precision obtained by using the proposed model. An application to Spanish Labour Force Survey data is also given.
dcterms:title
An Area-Level Model with Fixed or Random Domain Effects in Small Area Estimation Problems An Area-Level Model with Fixed or Random Domain Effects in Small Area Estimation Problems
skos:prefLabel
An Area-Level Model with Fixed or Random Domain Effects in Small Area Estimation Problems An Area-Level Model with Fixed or Random Domain Effects in Small Area Estimation Problems
skos:notation
RIV/68407700:21340/11:00187457!RIV12-MSM-21340___
n6:predkladatel
n11:orjk%3A21340
n3:aktivita
n9:Z n9:S
n3:aktivity
S, Z(MSM6840770039)
n3:dodaniDat
n20:2012
n3:domaciTvurceVysledku
n23:9287833
n3:druhVysledku
n14:C
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
185430
n3:idVysledku
RIV/68407700:21340/11:00187457
n3:jazykVysledku
n15:eng
n3:klicovaSlova
Small area estimation; linear mixed models; Fay-Herriot regression model; fixed effects; random effects; EBLUP; Labour Force Survey
n3:klicoveSlovo
n5:random%20effects n5:linear%20mixed%20models n5:Small%20area%20estimation n5:Labour%20Force%20Survey n5:Fay-Herriot%20regression%20model n5:EBLUP n5:fixed%20effects
n3:kontrolniKodProRIV
[D2C5021B5E63]
n3:mistoVydani
Berlin
n3:nazevZdroje
Modern Mathematical Tools and Techniques in Capturing Complexity
n3:obor
n12:BB
n3:pocetDomacichTvurcuVysledku
1
n3:pocetStranKnihy
511
n3:pocetTvurcuVysledku
4
n3:rokUplatneniVysledku
n20:2011
n3:tvurceVysledku
Esteban, M. D. Morales, D. Hobza, Tomáš Herrador, M.
n3:zamer
n19:MSM6840770039
s:numberOfPages
12
n22:doi
10.1007/978-3-642-20853-9_21
n18:hasPublisher
Springer-Verlag
n17:isbn
978-3-642-20852-2
n13:organizacniJednotka
21340