Covers a broad range of limit theorems for mathematical statistics, including proof methods and application techniques. Emphasizes manipulation of probability theorems to obtain statistical theorems. Imparts a knowledge of these basic statistical theorems, as well as an appreciation of the instrumental role of probability theory and a perspective on practical needs for its further development. Assumes introductory graduate knowledge of probability theory and mathematical statistics.
About the Author
ROBERT J. SERFLING, PhD, is a Professor at the Department of Mathematical Sciences at the University of Texas at Dallas.
Table of ContentsPreliminary Tools and Foundations.
The Basic Sample Statistics.
Transformation of Given Statistics.
Asymptotic Theory in Parametric Inference.
Von Mises Differentiable Statistical Functions.
Asymptotic Relative Efficiency.
Author & Subject Indexes.