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Mixed-Effects Models and Small Area Estimation (SpringerBriefs in Statistics) ペーパーバック – 2023/2/4
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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.
- 本の長さ129ページ
- 言語英語
- 出版社Springer
- 発売日2023/2/4
- 寸法15.5 x 0.76 x 23.5 cm
- ISBN-109811994854
- ISBN-13978-9811994852
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商品の説明
著者について
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.
登録情報
- 出版社 : Springer; 1st ed. 2023版 (2023/2/4)
- 発売日 : 2023/2/4
- 言語 : 英語
- ペーパーバック : 129ページ
- ISBN-10 : 9811994854
- ISBN-13 : 978-9811994852
- 寸法 : 15.5 x 0.76 x 23.5 cm
- Amazon 売れ筋ランキング: - 207,693位洋書 (洋書の売れ筋ランキングを見る)
- - 1,109位Probability & Statistics
- - 1,865位Professional Applied Mathematics
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