Theory and Methods of Statistics covers essential topics for advanced graduate students and professional research statisticians. This comprehensive resource covers many important areas in one manageable volume, including core subjects such as probability theory, mathematical statistics, and linear models, and various special topics, including nonparametrics, curve estimation, multivariate analysis, time series, and resampling. The book presents subjects such as "maximum likelihood and sufficiency," and is written with an intuitive, heuristic approach to build reader comprehension. It also includes many probability inequalities that are not only useful in the context of this text, but also as a resource for investigating convergence of statistical procedures.

Includes bibliographical references and index.

Online resource; title from PDF title page (SpringerLink, viewed July 8, 2016).

1. Probability theory -- 2. Some common probability distributions -- 3. Infinite sequences of random variables and their convergence properties -- 4. Basic concepts of statistical inference -- 5. Point estimation in parametric models -- 6. Hypothesis testing -- 7. Methods based on likelihood and their asymptotic properties -- 8. Distribution-free tests for hypothesis testing in nonparametric families -- 9. Curve estimation -- 10. Statistical functionals and their use in robust estimation -- 11. Linear models -- 12. Multivariate analysis -- 13. Time series.