Likelihood Methods in Statistics / Edition 1

Likelihood Methods in Statistics / Edition 1

by Thomas A. Severini
Pub. Date:
OUP Oxford


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Likelihood Methods in Statistics / Edition 1

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.

Product Details

ISBN-13: 9780198506508
Publisher: OUP Oxford
Publication date: 01/28/2001
Series: Oxford Statistical Science Series , #22
Edition description: New Edition
Pages: 392
Product dimensions: 5.80(w) x 8.90(h) x 0.80(d)

About the Author

Northwestern University

Table of Contents

1. Some basic concepts
2. Large-sample approximations
3. Likelihood
4. First-order asymptotic theory
5. Higher-order asymptotic theory
6. Asymptotic theory and conditional inference
7. The signed likelihood ratio statistic
8. Likelihood functions for a parameter of interest
9. The modified profile likelihood function
Appendix: Data sets used in the examples
Author index
Subject index

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