Likelihood Methods in Statistics

Zusatztext This book is an excellent account of likelihood-based statistical inference; I believe that it will be a very useful addition to any scholarly library. Klappentext This book provides an introduction to the modern theory of likelihood-based statistical inference. This theory is characterized by several important features. One is the recognition that it is desirable to condition on relevant ancillary statistics. Another is that probability approximations are based on saddlepoint and closely related approximations that generally have very high accuracy. A third aspect is that, for models with nuisance parameters, inference is often based on marginal or conditional likelihoods, or approximations to these likelihoods. These methods have been shown often to yield substantial improvements over classical methods. The book also provide an up-to-date account of recent results in the field, which has been undergoing rapid development. Zusammenfassung Likelihood methods play a central role in statistical theory and methodology. Recently, a new approach to likelihood inference has been developed that often leads to substantial improvements over classical methods. This book gives a detailed introduction to this modern theory of likelihood methods.

Dettagli sul prodotto

• Autori: Thomas A Severini, Thomas A. Severini, Thomas A. (Department of Statistics Severini, Severini Thomas A.
• Editore: Oxford University Press
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 09.11.2000
• EAN: 9780198506508
• ISBN: 978-0-19-850650-8
• Pagine: 392
• Serie: Oxford Statistical Science Series; Oxford Statistical Science Ser; Oxford Statistical Science Series; Oxford Statistical Science
• Categorie: Scienze naturali, medicina, informatica, tecnica > Matematica; MATHEMATICS / Probability & Statistics / General, Probability & statistics, Probability and statistics