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A course on small area estimation an...

Morales, Domingo.

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A course on small area estimation and mixed models = methods, theory and applications in R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	A course on small area estimation and mixed models/ by Domingo Morales ... [et al.].
Reminder of title:	methods, theory and applications in R /
other author:	Morales, Domingo.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xx, 599 p. :ill., digital ;24 cm.
[NT 15003449]:	1 Small Area Estimation -- 2 Design-based Direct Estimation -- 3 Design-based Indirect Estimation -- 4 Prediction Theory -- 5 Linear Models -- 6 Linear Mixed Models -- 7 Nested Error Regression Models -- 8 EBLUPs under Nested Error Regression Models -- 9 Mean Squared Error of EBLUPs -- 10 EBPs under Nested Error Regression Models -- 11 EBLUPs under Two-fold Nested Error Regression Models -- 12 EBPs under Two-fold Nested Error Regression Models -- 13 Random Regression Coefficient Models -- 14 EBPs under Unit-level Logit Mixed Models -- 15 EBPs under Unit-level Two-fold Logit Mixed Models -- 16 Fay-Herriot Models -- 17 Area-level Temporal Linear Mixed Models -- 18 Area-level Spatio-temporal Linear Mixed Models -- 19 Area-level Bivariate Linear Mixed Models -- 20 Area-level Poisson Mixed Models -- 21 Area-level Temporal Poisson Mixed Models -- A Some Useful Formulas -- Index.
Contained By:	Springer Nature eBook
Subject:	Small area statistics. -
Online resource:	https://doi.org/10.1007/978-3-030-63757-6
ISBN:	9783030637576

A course on small area estimation and mixed models = methods, theory and applications in R /
A course on small area estimation and mixed modelsmethods, theory and applications in R /[electronic resource] :by Domingo Morales ... [et al.]. - Cham :Springer International Publishing :2021. - xx, 599 p. :ill., digital ;24 cm. - Statistics for social and behavioral sciences,2199-7357. - Statistics for social and behavioral sciences..

1 Small Area Estimation -- 2 Design-based Direct Estimation -- 3 Design-based Indirect Estimation -- 4 Prediction Theory -- 5 Linear Models -- 6 Linear Mixed Models -- 7 Nested Error Regression Models -- 8 EBLUPs under Nested Error Regression Models -- 9 Mean Squared Error of EBLUPs -- 10 EBPs under Nested Error Regression Models -- 11 EBLUPs under Two-fold Nested Error Regression Models -- 12 EBPs under Two-fold Nested Error Regression Models -- 13 Random Regression Coefficient Models -- 14 EBPs under Unit-level Logit Mixed Models -- 15 EBPs under Unit-level Two-fold Logit Mixed Models -- 16 Fay-Herriot Models -- 17 Area-level Temporal Linear Mixed Models -- 18 Area-level Spatio-temporal Linear Mixed Models -- 19 Area-level Bivariate Linear Mixed Models -- 20 Area-level Poisson Mixed Models -- 21 Area-level Temporal Poisson Mixed Models -- A Some Useful Formulas -- Index.

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.

ISBN: 9783030637576

Standard No.: 10.1007/978-3-030-63757-6doiSubjects--Topical Terms:

2205889
Small area statistics.

LC Class. No.: QA276.6

Dewey Class. No.: 519.52

A course on small area estimation and mixed models = methods, theory and applications in R /
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245 0 2 $a A course on small area estimation and mixed models $h [electronic resource] : $b methods, theory and applications in R / $c by Domingo Morales ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xx, 599 p. : $b ill., digital ; $c 24 cm.
490 1 $a Statistics for social and behavioral sciences, $x 2199-7357
505 0 $a 1 Small Area Estimation -- 2 Design-based Direct Estimation -- 3 Design-based Indirect Estimation -- 4 Prediction Theory -- 5 Linear Models -- 6 Linear Mixed Models -- 7 Nested Error Regression Models -- 8 EBLUPs under Nested Error Regression Models -- 9 Mean Squared Error of EBLUPs -- 10 EBPs under Nested Error Regression Models -- 11 EBLUPs under Two-fold Nested Error Regression Models -- 12 EBPs under Two-fold Nested Error Regression Models -- 13 Random Regression Coefficient Models -- 14 EBPs under Unit-level Logit Mixed Models -- 15 EBPs under Unit-level Two-fold Logit Mixed Models -- 16 Fay-Herriot Models -- 17 Area-level Temporal Linear Mixed Models -- 18 Area-level Spatio-temporal Linear Mixed Models -- 19 Area-level Bivariate Linear Mixed Models -- 20 Area-level Poisson Mixed Models -- 21 Area-level Temporal Poisson Mixed Models -- A Some Useful Formulas -- Index.
520 $a 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.
650 0 $a Small area statistics. $3 2205889
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650 1 4 $a Statistics for Social Sciences, Humanities, Law. $3 3382004
650 2 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Statistics and Computing/Statistics Programs. $3 894293
650 2 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 3382132
700 1 $a Morales, Domingo. $3 3493186
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Statistics for social and behavioral sciences. $3 1567990
856 4 0 $u https://doi.org/10.1007/978-3-030-63757-6
950 $a Mathematics and Statistics (SpringerNature-11649)