Mixed-Effects Models and Small Area Estimation

This book provides a comprehensive introduction to mixed-effects models and small area estimation techniques, covering classical theory and recent methods. It discusses parameter estimation, random effects prediction, variable selection, and uncertainty measurement, with both frequentist and Bayesian approaches. It is useful for researchers and graduate students in data analysis and mathematical statistics.

Format: Paperback / softback
Length: 121 pages
Publication date: 04 February 2023
Publisher: Springer Verlag, Singapore

This comprehensive book offers a thorough introduction to mixed-effects models and small area estimation techniques. It delves into both the classical theory and the latest methods, providing a comprehensive understanding of these topics.

The book begins by addressing fundamental issues in mixed-effects models, including parameter estimation, random effects prediction, variable selection, and asymptotic theory. It explains how these models can be used to analyze data with both fixed and random effects, allowing for a more accurate representation of the relationships between variables.

Next, the standard mixed-effects models commonly employed in small area estimation, such as the Fay-Herriot model and the nested error regression model, are introduced. These models are used to estimate parameters of interest at the small area level, accounting for the spatial dependence of the data. Both frequentist and Bayesian approaches are discussed, providing readers with the flexibility to choose the most appropriate method based on their research objectives and assumptions.

To measure the uncertainty of the predictors, the book explores several methods to calculate mean squared errors and confidence intervals. It discusses the advantages and disadvantages of each method and helps readers select the most suitable approach for their data analysis.

Furthermore, the book introduces various advanced approaches using mixed-effects models, ranging from frequentist to Bayesian approaches. It discusses the benefits and limitations of each approach and provides examples of how they can be applied to real-world data sets.

This book is an invaluable resource for researchers and graduate students in fields requiring data analysis skills as well as in mathematical statistics. It provides a solid foundation in mixed-effects models and small area estimation, enabling readers to apply these techniques to their research and gain valuable insights into their data.

Weight: 214g
Dimension: 155 x 233 x 10 (mm)
ISBN-13: 9789811994852
Edition number: 1st ed. 2023