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Mean-squared errors of small area estimators under a multivariate linear model for repeated measures dataPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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2017 (English)Report (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Linköping: Linköping University Electronic Press, 2017. , p. 19
##### Series

LiTH-MAT-R, ISSN 0348-2960 ; 2017:05
##### Keywords [en]

Mean-squared errors, Multivariate linear model, Repeated measures data, Small area estamation
##### National Category

Probability Theory and Statistics
##### Identifiers

URN: urn:nbn:se:liu:diva-137113ISRN: LiTH-MAT-R--2017/05--SELibris ID: 20807903OAI: oai:DiVA.org:liu-137113DiVA, id: diva2:1093289
#####

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PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt616",{id:"formSmash:j_idt616",widgetVar:"widget_formSmash_j_idt616",multiple:true});
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##### In thesis

In this paper, we discuss the derivation of the first and second moments for the proposed small area estimators under a multivariate linear model for repeated measures data. The aim is to use these moments to estimate the mean-squared errors (MSE) for the predicted small area means as a measure of precision. A two stage estimator of MSE is obtained. At the first stage, we derive the MSE when the covariance matrices are known. To obtain an unbiased estimator of the MSE, at the second stage, a method based on parametric bootstrap is proposed for bias correction and for prediction error that reects the uncertainty when the unknown covariance is replaced by its suitable estimator.

1. Contributions to Small Area Estimation: Using Random Effects Growth Curve Model$(function(){PrimeFaces.cw("OverlayPanel","overlay1094130",{id:"formSmash:j_idt1061:0:j_idt1069",widgetVar:"overlay1094130",target:"formSmash:j_idt1061:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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