Mathematical Statistics: Asymptotic Minimax Theory
Author(s) (Product display):
Wayne State University, Detroit, MI;
California State University, Long Beach, CA
This book is designed to bridge the gap between traditional textbooks
in statistics and more advanced books that include the sophisticated
nonparametric techniques. It covers topics in parametric and nonparametric
large-sample estimation theory. The exposition is based on a collection of
relatively simple statistical models. It gives a thorough mathematical
analysis for each of them with all the rigorous proofs and explanations.
The book also includes a number of helpful exercises.
Prerequisites for the book include senior undergraduate/beginning
graduate-level courses in probability and statistics.
Book Series Name:
Graduate Studies in Mathematics
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Graduate students and research mathematicians interested in
This is a well written book and
should be of great interest to advanced graduate students/researchers in
mathematical statistics. The material is presented with great clarity by using
simple models as opposed to complex ones. ... Overall it should be of great
value to advanced graduate students and researchers in theoretical statistics.
The book can be recommended for libraries on campuses with a graduate program in statistics.