Asymptotic Statistics

"This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications." "Suitable as a text for a graduate or Master's level statistics course, this book also gives researchers in statistics, probability, and their applications an overview of the latest research in asymptotic statistics."--BOOK JACKET.  Read more... 1. Introduction -- 2. Stochastic Convergence -- 3. Delta Method -- 4. Moment Estimators -- 5. M- and Z-Estimators -- 6. Contiguity -- 7. Local Asymptotic Normality -- 8. Efficiency of Estimators -- 9. Limits of Experiments -- 10. Bayes Procedures -- 11. Projections -- 12. U-Statistics -- 13. Rank, Sign, and Permutation Statistics -- 14. Relative Efficiency of Tests -- 15. Efficiency of Tests -- 16. Likelihood Ratio Tests -- 17. Chi-Square Tests -- 18. Stochastic Convergence in Metric Spaces -- 19. Empirical Processes -- 20. Functional Delta Method -- 21. Quantiles and Order Statistics -- 22. L-Statistics -- 23. Bootstrap -- 24. Nonparametric Density Estimation -- 25. Semiparametric Models