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Cover: 9789811994852 | Mixed-Effects Models and Small Area Estimation | Sugasawa (u. a.)
Rückseite: 9789811994852 | Mixed-Effects Models and Small Area Estimation | Sugasawa (u. a.)
Cover: 9789811994852 | Mixed-Effects Models and Small Area Estimation | Sugasawa (u. a.)
Rückseite: 9789811994852 | Mixed-Effects Models and Small Area Estimation | Sugasawa (u. a.)
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Mixed-Effects Models and Small Area Estimation
Taschenbuch von Shonosuke Sugasawa (u. a.)
Sprache: Englisch

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Beschreibung
This book provides a self-contained introduction of mixed-effects models and small area estimation techniques. In particular, it focuses on both introducing classical theory and reviewing the latest methods. First, basic issues of mixed-effects models, such as parameter estimation, random effects prediction, variable selection, and asymptotic theory, are introduced. Standard mixed-effects models used in small area estimation, known as the Fay-Herriot model and the nested error regression model, are then introduced. Both frequentist and Bayesian approaches are given to compute predictors of small area parameters of interest. For measuring uncertainty of the predictors, several methods to calculate mean squared errors and confidence intervals are discussed. Various advanced approaches using mixed-effects models are introduced, from frequentist to Bayesian approaches. This book is helpful for researchers and graduate students in fields requiring data analysis skills as well as in mathematical statistics.
This book provides a self-contained introduction of mixed-effects models and small area estimation techniques. In particular, it focuses on both introducing classical theory and reviewing the latest methods. First, basic issues of mixed-effects models, such as parameter estimation, random effects prediction, variable selection, and asymptotic theory, are introduced. Standard mixed-effects models used in small area estimation, known as the Fay-Herriot model and the nested error regression model, are then introduced. Both frequentist and Bayesian approaches are given to compute predictors of small area parameters of interest. For measuring uncertainty of the predictors, several methods to calculate mean squared errors and confidence intervals are discussed. Various advanced approaches using mixed-effects models are introduced, from frequentist to Bayesian approaches. This book is helpful for researchers and graduate students in fields requiring data analysis skills as well as in mathematical statistics.
Über den Autor
Shonosuke Sugasawa is an Associate Professor in the Center for Spatial Information Science at the University of Tokyo. His research interests include Bayesian modeling, spatial statistics and mixed-effects modeling.

Tatsuya Kubokawa is a Professor in the Faculty of Economics at the University of Tokyo. His research interests include statistical decision theory, multivariate analysis and mixed-effects modeling.
Zusammenfassung

Introduces not only classical theory of mixed-effects models but also recently proposed techniques

Explains in detail self-contained theory and methods of mixed-effects models adopted in small area

Illustrates several numerical examples for readers' understanding of mixed-effects models and small area estimation

Inhaltsverzeichnis

Introduction.- General Mixed-Effects Models and BLUP.- Measuring Uncertainty of Predictors.- Basic mixed-effects Models for Small Area Estimation.- Hypothesis Tests and Variable Selection.- Advanced Theory of Basic Small Area Models.- Small Area Models for Non-normal Response Variables.- Extensions of Basic Small Area Models.

Details
Erscheinungsjahr: 2023
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Importe, Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Reihe: SpringerBriefs in Statistics
Inhalt: viii
121 S.
1 s/w Illustr.
121 p. 1 illus.
ISBN-13: 9789811994852
ISBN-10: 9811994854
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Sugasawa, Shonosuke
Kubokawa, Tatsuya
Auflage: 1st edition 2023
Hersteller: Springer
Springer Singapore
SpringerBriefs in Statistics
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 235 x 155 x 8 mm
Von/Mit: Shonosuke Sugasawa (u. a.)
Erscheinungsdatum: 04.02.2023
Gewicht: 0,213 kg
Artikel-ID: 125978473
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