Mixed-Effects Models and Small Area Estimation (SpringerBriefs in Statistics) - Softcover

Sugasawa, Shonosuke; Kubokawa, Tatsuya

Softcover

ISBN 10: 9811994854 ISBN 13: 9789811994852

Publisher: Springer, 2023

Synopsis

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.

About this title

About the Author

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. 

Edition details

• Publisher: Springer
• Publication date: 2023
• Language: English
• ISBN 10: 9811994854
• ISBN 13: 9789811994852
• Binding: Paperback
• Edition number: 1
• Number of pages: 129