Small area estimation under random regression coefficient models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F13%3A00199082" target="_blank" >RIV/68407700:21340/13:00199082 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1080/00949655.2012.684094" target="_blank" >http://dx.doi.org/10.1080/00949655.2012.684094</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/00949655.2012.684094" target="_blank" >10.1080/00949655.2012.684094</a>

Result language
angličtina
Original language name
Small area estimation under random regression coefficient models
Original language description
Statistical agencies are interested to report precise estimates of linear parameters from small areas. This goal can be achieved by using model-based inference. In this sense, random regression coefficient models provide a flexible way of modelling the relationship between the target and the auxiliary variables. Because of this, empirical best linear unbiased predictor (EBLUP) estimates based on these models are introduced. A closed-formula procedure to estimate the mean-squared error of the EBLUP estimators is also given and empirically studied. Results of several simulation studies are reported as well as an application to the estimation of household normalized net annual incomes in the Spanish Living Conditions Survey.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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