Mathematical Statistics: Basic Ideas and Selected Topics

This is definitely not an easy book, and there're typos in this book, but all of this can not stop this book to be an excellent book. My professor has been working with this book for years, and he has published some solution for this book, but he still does not think that he has solved every prolem in this book completely or perfectly, and he can still find some interesting solutions from students. This book is just so illuminating. This book would serve as an excellent reference and also the textbook.

This new version will become a classic. Like its predecessor, the 1977 edition, it is an important reference that is at the forefront of contemporary statistical knowledge and will "stay young" for many years to come. The book gives a rigourous and carefully detailed presentation of estimation and testing, in the univariate and multivariate setting. Moreover, it presents both the frequentist and the bayesian viewpoint, covers asymptotics and deals with algorithmic issues (e.g. the EM algorithm). On top of theory, practical issues are raised all over the text; the many examples and problems are very relevant for applications. The curious reader can learn the "why" of many popular statistical procedures of much use nowadays, like logistic regression, e.g.. Any statistician with quantitative background should have it. This a top statistical text, and I have used it very often, both for my classes and for my own research.

A classic, time-honored introduction to the theory and practice of statistics modeling and inference-revised to reflect the changing focus of Statistics and the mathematics background of today's students. Coverage begins with the more general nonparametric point of view and then looks at parametric models as submodels of the nonparametric ones which can be described smoothly by Euclidean parameters. Although some computational issues are discussed, this is very much a book on theory. A second volume treating more advanced topics is in preparation

As a graduate level book, it renders main results in statistics at an advanced level readily accessible. Topics range from estimation theory to asymptotic analysis. Ideal for mathematical statician.