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dc.contributor.authorOguz-Alper, Melike
dc.contributor.authorBerger, Yves G.
dc.date.accessioned2022-03-29T14:40:45Z
dc.date.available2022-03-29T14:40:45Z
dc.date.created2020-04-29T16:38:33Z
dc.date.issued2020
dc.identifier.citationComputational Statistics & Data Analysis. 2020, 145 (May), .en_US
dc.identifier.issn0167-9473
dc.identifier.urihttps://hdl.handle.net/11250/2988425
dc.description.abstractData used in social, behavioural, health or biological sciences may have a hierarchical structure due to the population of interest or the sampling design. Multilevel or marginal models are often used to analyse such hierarchical data. These data are often selected with unequal probabilities from a clustered and stratified population. An empirical likelihood approach for the regression parameters of a multilevel model is proposed. It has the advantage of taking into account of the sampling design. This approach can be used for point estimation, hypothesis testing and confidence intervals for the sub-vector of parameters. It provides asymptotically valid inference for small and large sampling fractions. The simulation study shows the advantages of the empirical likelihood approach over alternative parametric approaches. The approach proposed is illustrated using the Programme for International Student Assessment (PISA) survey dataen_US
dc.language.isoengen_US
dc.titleModelling multilevel data under complex sampling designs: An empirical likelihood approachen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionsubmittedVersionen_US
dc.rights.holderElsevieren_US
dc.source.pagenumber16en_US
dc.source.volume145en_US
dc.source.journalComputational Statistics & Data Analysisen_US
dc.source.issueMayen_US
dc.identifier.doi10.1016/j.csda.2019.106906
dc.identifier.cristin1808703
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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