Statistical Inference
An Integrated Approach
Hodder Arnold (Publisher)
1st Edition
Published on 30. July 1999
Book
Hardback
272 pages
978-0-340-74059-0 (ISBN)
Description
Presenting an integrated approach to Statistical Inference at a graduate level, this book discusses and compares the two main schools of statistical thought: frequentist and Bayesian. It covers such subjects as point and interval estimation, hypotheses testing and prediction, while also exploring recent computationally-intensive methods. "Statistical Inference" ideally compliments "Bayesian Statistics" and "Kendall's Advanced Theory of Statistics, Volumes 1, 2A" and "2B". It will prove invaluable to postgraduates in the fields of statistics, operations research, mathematics and economics. Applied statisticians will also find it a useful reference tool and a source of information on newer developments.
Reviews / Votes
The exercises are well thought through...the book would be useful even for less mathematically orientated users of statistical methods, as it presents important ideas not often found in a textbook. -- Statistical Methods in Medical Research It would make an excellent text for a masters level inference course that aimed to cover classical and Bayesian inference. -- BiometricsMore details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
16ill.
Dimensions
Height: 235 mm
Width: 156 mm
ISBN-13
978-0-340-74059-0 (9780340740590)
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Schweitzer Classification
Other editions
New editions
Helio S. Migon | Dani Gamerman | Francisco Louzada
Statistical Inference
An Integrated Approach, Second Edition
Book
09/2014
2nd Edition
Chapman & Hall/CRC
€146.60
Shipment within 15-20 days
Person
H Migon, D Gamerman, Both at the University of Rio de Janerio, Brazil
Content
Information; the concept of probability; linear algebra and probabilities; elements of inference; common statistical models; likelihood based functions; Bayes theorem; exchangeability; sufficiency and exponential family; parameter elimination; prior distribution; entirely subjective specification; specification through functional forms; conjugacy with the exponential family; the main conjugate families; non-informative priors; hierarchical priors; estimation; introduction to decision theory; classical point estimation; criterion of estimators; interval estimation; estimation in the normal model; approximate methods; general problem of inference; optimization techniques; analytical approximations; numerical integration methods; simulation methods; hypothesis testing; classical hypothesis testing; Bayesian hypothesis testing; hypothesis testing and confidence intervals; asymptotic tests; prediction; Bayesian prediction; classical prediction; prediction in the linear model; linear prediction; introduction to linear models; classical linear models; Bayesian linear models; hierarchical linear models; dynamic linear models.