Volume I presents fundamental, classical statistical concepts at the doctorate level without using measure theory. It gives careful proofs of major results and explains how the theory sheds light on the properties of practical methods. Volume II covers a number of topics that are important in current measure theory and practice. It emphasizes nonparametric methods which can really only be implemented with modern computing power on large and complex data sets. In addition, the set includes a large number of problems with more difficult ones appearing with hints and partial solutions for the instructor.
Table of Contents
Volume I: Statistical Models, Goals, and Performance Criteria. Methods of Estimation. Measures of Performance. Testing and Confidence Regions. Asymptotic Approximations. Inference in the Multiparameter Case. Appendices. Index. Volume II: Introduction to Volume II. Inference When the Number of Parameters Is Large. Distribution-Free, Unbiased and Equivariant Procedures. Inference in Semiparametric Models. Monte Carlo Methods. Nonparametric Inference for Functions of One Variable. Prediction and Machine Learning. Appendix D. Supplements to Text. Solutions for Vol. II. Subject Index.
"While other general statistics texts at a similar level touch on some of the topics covered in this book, none of them cover the modern material in this book with comparable depth. As such it is certainly a valuable contribution to our advanced literature on theoretical statistics."
~RobertW. Keener, University of Michigan