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Essentials of Statistical Inference

1 rating: 3.0
A book by G. A. Young

"This is a delightful book! It gives a well-written exposure to inference issues in statistics, very suitable for a first-year graduate course...The authors present the material in a very good pedagogical manner. The examples are excellent, and the exercises … see full wiki

Tags: Books
Author: G. A. Young
Publisher: Cambridge University Press
1 review about Essentials of Statistical Inference

Ideal for advanced classes in the mathematical justification of statistical inference techniques

  • May 13, 2007
Statistical inference is arguably the most fundamental statistical technique used in the world. When I teach a basic statistics course, we spend half the term doing inference problems. The formal theory behind inference is an integral part of mathematics and this book covers the key ideas of the Bayesian, frequentist and Fisherian approaches. Advertised as written at the level of advanced undergraduates and graduate students, I concur with that assessment. The material is accessible to the senior math major, although I disagree with the sentence on the cover, "Some prior knowledge of probability is assumed, while some previous knowledge of the objectives and main approaches to statistical inference would be helpful but is not essential." To understand this book, it is necessary that the reader have a good understanding of what inference is and better yet, should have already done several inference problems. Experience in multivariable calculus and the functions of commonly used statistical distributions are also essential.
Some of the more advanced topics include:

*) Bayesian computation.
*) Higher-order liklihood theory.
*) Predictive inference.
*) Bootstrap methods.
*) Conditional inference.

This is a solid book, ideal for advanced classes in the mathematical justification for statistical inference

Published in Journal of Recreational Mathematics, reprinted with permission

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