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Mixed-effects models and small area ...

Sugasawa, Shonosuke.

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Mixed-effects models and small area estimation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mixed-effects models and small area estimation/ by Shonosuke Sugasawa, Tatsuya Kubokawa.
Author:	Sugasawa, Shonosuke.
other author:	Kubokawa, Tatsuya.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	viii, 121 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Multilevel models (Statistics) -
Online resource:	https://doi.org/10.1007/978-981-19-9486-9
ISBN:	9789811994869

Mixed-effects models and small area estimation
Sugasawa, Shonosuke.

Mixed-effects models and small area estimation[electronic resource] /by Shonosuke Sugasawa, Tatsuya Kubokawa. - Singapore :Springer Nature Singapore :2023. - viii, 121 p. :ill., digital ;24 cm. - SpringerBriefs in statistics. JSS research series in statistics. - SpringerBriefs in statistics.JSS research series in statistics..

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.

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.

ISBN: 9789811994869

Standard No.: 10.1007/978-981-19-9486-9doiSubjects--Topical Terms:

604810
Multilevel models (Statistics)

LC Class. No.: QA276

Dewey Class. No.: 519.5

Mixed-effects models and small area estimation
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520 $a 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.
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