Asymptotic Theory of Statistics and Probability

- Basic Convergence Concepts and Theorems
- Metrics, Information Theory, Convergence, and Poisson Approximations
- More General Weak and Strong Laws and the Delta Theorem
- Transformations
- More General Clts
- Moment Convergence and Uniform Integrability
- Sample Percentiles and Order Statistics
- Sample Extremes
- Central Limit theorems for Dependent Sequences
- Central Limit Theorem for Markov Chains
- Accuracy of Clts
- Invariance Principles
- Edgeworth Expansions and Cumulants
- Saddlepoint Approximations
- U-Statistics
- Maximum Likelihood Estimates
- M Estimates
- the Trimmed Mean
- Multivariate Location Parameter and Multivariate Medians
- Bayes Procedures and Posterior Distributions
- Testing Problems
- Asymptotic Efficiency in Testing
- Some General Large Deviation Results
- Classical Nonparametrics
- Two-Sample Problems
- Goodness of Fit
- Chi-Square Tests for Goodness of Fit
- Goodness of Fit With Estimated Parameters
- The Bootstrap
- Jackknife
- Permutation Tests
- Density Estimation
- Mixture Models and Nonparametric Deconvolution
- High Dimensional Inference and False Discovery

From the reviews:

"This is definitely not your typical book on theory. The approach that Dasgupta has taken with this far-reaching volume is to explore important results and applications of asymptotic theory without emphasizing the intricate mathematical details. The focus is on the forest rather than on the trees, and this results in a readable text that, for the most part, should be accessible to anyone with a first-year graduate-level course in statistical theory.

I would imagine that this book woult be very useful as a first place to look for help in solving many problems in asymptotics. The book can provide an overview of the key issues, some ideas, and a path to more detail." (Biometrics, September 2008)

"Presents an encyclopedic treatment of classic as well as contemporary large sample theory, including both statistical problems and probabilistic issues and tools." (Journal of Economic Literature, Volume 46, no. 3, 2008)

"... a nice handbook and reference material. This is a different book on the asymptotic theory and its use in probability and statistical inference. It covers a wide range of divergent topics where the large sample theory is useful and can be naturally applied. ... The book is will organized and clearly written. The book works well as a reference text for a theoretical statistician working with the asymptotics. It can also be used as a textbook for several topics of the graduate courses." (International Statistical Review,2009, 77, 1)

"This book provides a comprehensive overview of asymptotic theory in probability and mathematical statistics. ... The text is written in a very clear style ... . the book is a very good choice as a first reading. It also contains a large collection of inequalities from linear algebra, probability and analysis that are of importance in mathematical statistics. This collection makes the volume even more valuable as a reference for students as well as research workers."