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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.
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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