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dc.contributor.authorSchoch, Tobias
dc.date.accessioned2015-10-05T15:43:33Z
dc.date.available2015-10-05T15:43:33Z
dc.date.issued2011-12-01T00:00:00Z
dc.identifier.urihttp://hdl.handle.net/11654/9969
dc.description.abstractSmall area estimation is a topic of increasing importance in official statistics. Although the classical EBLUP method is useful for estimating the small area means efficiently under the normality assumptions, it can be highly influenced by the presence of outliers. Therefore, Sinha and Rao (2009; The Canadian Journal of Statistics) proposed robust estimators/predictors for a large class of unit- and area-level models. We confine attention to the basic unit-level model and discuss a related, but slightly different, robustification. In particular, we develop a fast algorithm that avoids inversion and multiplication of large matrices, and thus permits the user to apply the method to large datasets. In addition, we derive much simpler expressions of the bounded-influence predicting equations to robustly predict the small-area means than Sinha and Rao (2009) did.
dc.language.isoen_UK
dc.accessRightsAnonymous
dc.subjectstatistics
dc.subject.ddc330 - Wirtschaft
dc.subject.ddc659 - Werbung & Public Releations (PR)
dc.titleRobust Unit-Level Small Area Estimation: A Fast Algorithm for Large Datasets.
dc.type01 - Zeitschriftenartikel, Journalartikel oder Magazin
dc.subtitleAustrian Journal of Statistics
dc.audienceSonstige
fhnw.publicationStateUnveröffentlicht
fhnw.ReviewTypeAnonymer ex ante Peer Review der vollständigen Publikation
fhnw.InventedHereunbekannt


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