Likelihood Methods in Statistics (Hardcover)

Thomas A. Severini

ISBN 10: 0198506503 / ISBN 13: 9780198506508

Published by Oxford University Press, Oxford, 2000

About this Item

Hardcover. 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 isoften 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 bookalso provides an up-to-date account of recent results in the field, which has been undergoing rapid development. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9780198506508

Bibliographic Details

• Title: Likelihood Methods in Statistics (Hardcover)
• Publisher: Oxford University Press, Oxford
• Publication Date: 2000
• Language: English
• ISBN 10: 0198506503
• ISBN 13: 9780198506508
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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.

About the Author

Thomas A. Severini is at Northwestern University Evanston, Illinois.