The Complete Guide to Statistical Theory, Simulation and Probability

This book is a unique compendium of core topics in the Statistics Ph.D. program, as well as covering the latest statistical theory, graduate probability and simulation techniques. gives an elaborate treatment of standard statistics topics such as parametric inference, basic theory of linear models, large sample theory, as well as more advanced topics such as robust estimation, density estimation, bootstrap, multiple testing, and the latest breakthrough developments such as the LASSO and thresholding and regularization. It also gives self-contained treatments of standard graduate probability and major Monte Carlo techniques, including MCMC. As such, this book can be used as an all-purpose text in the statistics Ph.D. programs, as well as a unique research reference.

The book provides 918 exercises, and an additional 98 exercises at the end of the book. The book also has a complete and comprehensive synopsis of real analysis, calculus, linear algebra and matrix theory as an invaluable source of consultation for students, instructors, and researchers.