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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...
Autores principales: | , , , |
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Lenguaje: | eng |
Publicado: |
Springer
2021
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-030-63757-6 http://cds.cern.ch/record/2758284 |
_version_ | 1780970114576809984 |
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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. . |
id | cern-2758284 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2021 |
publisher | Springer |
record_format | invenio |
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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