A Course on Small Area Estimation and Mixed Models: Methods, Theory and Applications in R

This advanced textbook covers small area estimation techniques, mathematical and statistical theory, and practical implementation with R code. It is useful for students, researchers, and applied statisticians, providing sample applications and rigorous theory.

Format: Paperback / softback
Length: 599 pages
Publication date: 14 March 2022
Publisher: Springer Nature Switzerland AG

This comprehensive textbook delves into advanced techniques for small area estimation, providing a thorough exploration of the underlying mathematical and statistical principles. It offers practical guidance on implementing these methods, accompanied by detailed explanations and comparisons of various statistical methodologies. By presenting the theory in a rigorous manner, the book aids readers in understanding how to develop innovative approaches for small area estimation. Additionally, it includes numerous real-world sample applications, showcasing the practical applications of small area estimation techniques across diverse fields. The text includes comprehensive R code, accompanied by detailed explanations and applications to four datasets that mimic data from labor markets and living conditions surveys. These datasets include socioeconomic indicators such as total unemployment, unemployment rates, average annual household incomes, and poverty indicators. With its extensive coverage and practical insights, this book is an invaluable resource for master and PhD students in statistics and applied statistics, as well as for official and other practitioners in the field.

Weight: 934g
Dimension: 157 x 236 x 36 (mm)
ISBN-13: 9783030637590
Edition number: 1st ed. 2021