Theory Of Statistics
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Theory of Statistics
Author: Mark J. Schervish
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
The aim of this graduate textbook is to provide a comprehensive advanced course in the theory of statistics covering those topics in estimation, testing, and large sample theory which a graduate student might typically need to learn as preparation for work on a Ph.D. An important strength of this book is that it provides a mathematically rigorous and even-handed account of both Classical and Bayesian inference in order to give readers a broad perspective. For example, the "uniformly most powerful" approach to testing is contrasted with available decision-theoretic approaches.
Theory and Methods of Statistics
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.
Statistics in Theory and Practice
Author: Robert Lupton
language: en
Publisher: Princeton University Press
Release Date: 1993-08
Aimed at readers without a specialist scientific background, this monograph describes the theory underlying classical statistical methods. Readers with some familiarity of the standard tests will learn more about their strengths, weaknesses and domains of applicability.