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A course on small area estimation and mixed models: methods, theory and applications in r

This advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers un...

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Detalles Bibliográficos
Autores principales: Morales, Domingo, Esteban, María Dolores, Pérez, Agustín, Hobza, Tomáš
Lenguaje:eng
Publicado: Springer 2021
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-030-63757-6
http://cds.cern.ch/record/2758284
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author Morales, Domingo
Esteban, María Dolores
Pérez, Agustín
Hobza, Tomáš
author_facet Morales, Domingo
Esteban, María Dolores
Pérez, Agustín
Hobza, Tomáš
author_sort Morales, Domingo
collection CERN
description This advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new methodologies for small area estimation. It also includes numerous sample applications of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets and living conditions surveys, where the socioeconomic indicators include the small area estimation of total unemployment, unemployment rates, average annual household incomes and poverty indicators. Given its scope, the book will be useful for master and PhD students, and for official and other applied statisticians. .
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institution Organización Europea para la Investigación Nuclear
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spelling cern-27582842021-04-21T16:40:35Zdoi:10.1007/978-3-030-63757-6http://cds.cern.ch/record/2758284engMorales, DomingoEsteban, María DoloresPérez, AgustínHobza, TomášA course on small area estimation and mixed models: methods, theory and applications in rMathematical Physics and MathematicsThis advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new methodologies for small area estimation. It also includes numerous sample applications of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets and living conditions surveys, where the socioeconomic indicators include the small area estimation of total unemployment, unemployment rates, average annual household incomes and poverty indicators. Given its scope, the book will be useful for master and PhD students, and for official and other applied statisticians. .Springeroai:cds.cern.ch:27582842021
spellingShingle Mathematical Physics and Mathematics
Morales, Domingo
Esteban, María Dolores
Pérez, Agustín
Hobza, Tomáš
A course on small area estimation and mixed models: methods, theory and applications in r
title A course on small area estimation and mixed models: methods, theory and applications in r
title_full A course on small area estimation and mixed models: methods, theory and applications in r
title_fullStr A course on small area estimation and mixed models: methods, theory and applications in r
title_full_unstemmed A course on small area estimation and mixed models: methods, theory and applications in r
title_short A course on small area estimation and mixed models: methods, theory and applications in r
title_sort course on small area estimation and mixed models: methods, theory and applications in r
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-030-63757-6
http://cds.cern.ch/record/2758284
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