The aim of this graduate textbook is to provide a comprehensive advanced
course in the theory of statistics covering those topics in estimation,
testing, and large sample theory which a graduate student might
typically need to learn as preparation for work on a Ph.D. An important
strength of this book is that it provides a mathematically rigorous and
even-handed account of both Classical and Bayesian inference in order to
give readers a broad perspective. For example, the "uniformly most
powerful" approach to testing is contrasted with available
decision-theoretic approaches.