Essentials of Statistical Inference

G. A. Young

ISBN 10: 0521548667 / ISBN 13: 9780521548663

Edité par Cambridge University Press, 2010

A propos de cet article

2010. 1st Edition. Paperback. This textbook presents the concepts and results underlying the Bayesian, frequentist, and Fisherian approaches to statistical inference. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 236 pages, 92 exercises. BIC Classification: PBT. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 253 x 179 x 28. Weight in Grams: 410. Series: Cambridge Series in Statistical and Probabilistic Mathematics. 236 pages, 92 exercises. This 2005 textbook presents the concepts and results underlying the Bayesian, frequentist, and Fisherian approaches to statistical inference. Cateogry: (P) Professional & Vocational; (U) Tertiary Education (US: College). BIC Classification: PBT. Dimension: 253 x 179 x 28. Weight: 438. . . . . .

N° de réf. du vendeur V9780521548663

Détails bibliographiques

• Titre: Essentials of Statistical Inference
• Éditeur: Cambridge University Press
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 0521548667
• ISBN à 13 chiffres: 9780521548663
• Reliure: Couverture souple
• Édition: Edition originale
• État de l'article: New

A propos de ce titre

Synopsis

Aimed at advanced undergraduate and graduate students in mathematics and related disciplines, this book presents the concepts and results underlying the Bayesian, frequentist and Fisherian approaches, with particular emphasis on the contrasts between them. Computational ideas are explained, as well as basic mathematical theory. Written in a lucid and informal style, this concise text provides both basic material on the main approaches to inference, as well as more advanced material on developments in statistical theory, including: material on Bayesian computation, such as MCMC, higher-order likelihood theory, predictive inference, bootstrap methods and conditional inference. It contains numerous extended examples of the application of formal inference techniques to real data, as well as historical commentary on the development of the subject. Throughout, the text concentrates on concepts, rather than mathematical detail, while maintaining appropriate levels of formality. Each chapter ends with a set of accessible problems.

À propos des auteurs

G. A. Young is Professor of Statistics at Imperial College London.

R. L. Smith is Mark L. Reed Distinguished Professor of Statistics at the University of North Carolina, Chapel Hill.