Small area estimation – introduction, models and real data application
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F24%3A00379561" target="_blank" >RIV/68407700:21340/24:00379561 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Small area estimation – introduction, models and real data application
Popis výsledku v původním jazyce
Small Area Estimation (SAE) is a branch of mathematical statistics that deals with the problem of estimating population parameters in subsets (called areas or domains) of a population where the sample sizes are not large enough to provide reliable direct estimates. For this purpose, SAE introduces statistical models that “borrow strength” from related small areas, data from external administrative sources, or data from different time periods. An overview of basic principles, models and problems encountered in SAE is given in the first part of the presentation. Then, the ideas are illustrated by an application of a unit-level multinomial mixed model to real data from the first Spanish Labour Force Survey of 2021, where the target is to map labour force indicators by province, sex, and age group.
Název v anglickém jazyce
Small area estimation – introduction, models and real data application
Popis výsledku anglicky
Small Area Estimation (SAE) is a branch of mathematical statistics that deals with the problem of estimating population parameters in subsets (called areas or domains) of a population where the sample sizes are not large enough to provide reliable direct estimates. For this purpose, SAE introduces statistical models that “borrow strength” from related small areas, data from external administrative sources, or data from different time periods. An overview of basic principles, models and problems encountered in SAE is given in the first part of the presentation. Then, the ideas are illustrated by an application of a unit-level multinomial mixed model to real data from the first Spanish Labour Force Survey of 2021, where the target is to map labour force indicators by province, sex, and age group.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
10103 - Statistics and probability
Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů