Likelihood Methods in Statistics

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.


Author: Thomas A. Severini
Publisher: Oxford University Press, USA
Published: 01/18/2001
Pages: 392
Binding Type: Hardcover
Weight: 1.61lbs
Size: 9.32h x 6.32w x 1.08d
ISBN: 9780198506508

Review Citation(s):
Scitech Book News 06/01/2001 pg. 41

This title is not returnable