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.

Readership

Graduate students and research mathematicians interested in mathematical statistics.