Mixed‐Effects Models and Small Area EstimationShonosukeSugasawa and TatsuyaKubokavaSpringer Nature, 2023, viii + 121 pages, £39.99, paperback ISBN: 978‐981‐19‐9485‐2
本书介绍混合模型的基本理论及其在小区域估计中的应用，重点讲解Fay-Herriot和嵌套误差回归模型，涵盖频率学派和贝叶斯方法，适合研究生和研究人员提升对小区域估计理论的理解。
Readership: Master's level students, PhD students, researchers at universities and research institutes. The book introduces the fundamental theory of mixed models and its application in the context of small area estimation. The primary focus is on the Fay–Herriot and nested error regression models, utilising both frequentist and Bayesian methodologies. The book is organised into eight chapters, covering critical topics in advanced theory, the theory of non-normal response variables, and various extensions. These extensions encompass considerations such as measurement errors, non-parametric and semiparametric models, as well as the effects of variance heterogeneity. Each chapter concludes with an exhaustive list of references. The book provides an intriguing and highly valuable presentation of the latest methodological advancements in the field, coupled with various extensions. Nonetheless, there is potential for enhancing reader-friendliness. For example, creating the exercise collections to the end of each chapter, incorporating more graphical illustrations—particularly for examples—and possibly introducing the computational tools used could significantly enhance the book's comprehensibility, especially for educational purposes. Nevertheless, the book comes with a strong recommendation for individuals aiming to elevate their grasp of small area estimation theory and its most current trends.