Statistical Estimation: Asymptotic Theory

This book delves into the intricate world of statistical estimation, focusing on the asymptotic theory that underpins modern statistical analysis. With a blend of theoretical rigor and practical implications, the authors explore how estimators behave as sample sizes grow, revealing vital insights into their efficiency and consistency.

The authors, I.A. Ibragimov, R Z Has'minskii, and S Kotz bring a wealth of knowledge and experience to the table, offering readers a comprehensive understanding of the limiting behavior of estimators. Readers will find a rich exposition of various estimation techniques, complete with mathematical formulations that clarify the underlying concepts.

Through carefully constructed examples and theoretical discussions, the text sheds light on the connection between large sample properties and finite sample behavior, making it an invaluable resource for statisticians and researchers looking to deepen their understanding of asymptotic methods in statistical estimation.