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This book involves ideas/results from the topics of mathematical,
information, and data sciences, in connection with the main
research interests of Professor Pardo that can be summarized as
Information Theory with Applications to Statistical Inference. This
book is a tribute to Professor Leandro Pardo, who has chaired the
Department of Statistics and OR of the Complutense University in
Madrid, and he has been also President of the Spanish Society of
Statistics and Operations Research. In this way, the contributions
have been structured into three parts, which often overlap to a
greater or lesser extent, namely Trends in Mathematical Sciences
(Part I) Trends in Information Sciences (Part II) Trends in Data
Sciences (Part III) The contributions gathered in this book have
offered either new developments from a theoretical and/or
computational and/or applied point of view, or reviews of recent
literature of outstanding developments. They have been applied
through nice examples in climatology, chemistry, economics,
engineering, geology, health sciences, physics, pandemics, and
socioeconomic indicators. Consequently, the intended audience of
this book is mainly statisticians, mathematicians, computer
scientists, and so on, but users of these disciplines as well as
experts in the involved applications may certainly find this book a
very interesting read.
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.
This book involves ideas/results from the topics of mathematical,
information, and data sciences, in connection with the main
research interests of Professor Pardo that can be summarized as
Information Theory with Applications to Statistical Inference. This
book is a tribute to Professor Leandro Pardo, who has chaired the
Department of Statistics and OR of the Complutense University in
Madrid, and he has been also President of the Spanish Society of
Statistics and Operations Research. In this way, the
contributions have been structured into three parts, which often
overlap to a greater or lesser extent, namely Trends in
Mathematical Sciences (Part I) Trends in Information Sciences (Part
II) Trends in Data Sciences (Part III) The contributions gathered
in this book have offered either new developments from a
theoretical and/or computational and/or applied point of view, or
reviews of recent literature of outstanding developments. They have
been applied through nice examples in climatology, chemistry,
economics, engineering, geology, health sciences, physics,
pandemics, and socioeconomic indicators. Consequently, the intended
audience of this book is mainly statisticians, mathematicians,
computer scientists, and so on, but users of these disciplines as
well as experts in the involved applications may certainly find
this book a very interesting read.
This scarce antiquarian book is a selection from Kessinger
Publishing's Legacy Reprint Series. Due to its age, it may contain
imperfections such as marks, notations, marginalia and flawed
pages. Because we believe this work is culturally important, we
have made it available as part of our commitment to protecting,
preserving, and promoting the world's literature. Kessinger
Publishing is the place to find hundreds of thousands of rare and
hard-to-find books with something of interest for everyone
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