Author Description
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.
Menrva's Summary
THE THESIS
This text investigates the modern theoretical framework of likelihood-based statistical inference and its practical application in complex modeling scenarios. Thomas A. Severini presents a comprehensive overview of the field, focusing on the evolution of statistical methods that prioritize accuracy and conditional analysis. The book synthesizes advanced mathematical concepts to demonstrate how modern techniques improve upon classical statistical approaches, particularly in the presence of nuisance parameters.
THE SCOPE MAP
What You Will Find
- Theoretical foundations of likelihood-based inference
- Application of saddlepoint and related probability approximations
- Methodologies for handling nuisance parameters through marginal and conditional likelihoods
- Analysis of recent developments and contemporary research results in statistical science
THE AUTHORITY PERSPECTIVE
Experts recognize this work as a rigorous and technical resource for graduate-level students and researchers in statistics. Readers frequently note the mathematical density of the prose, which serves as a foundational reference for those seeking to understand high-accuracy statistical approximations.
Page Count:
392
Publication Date:
2001-01-18
Publisher:
Oxford University Press
ISBN-10:
0198506503
ISBN-13:
9780198506508
Science
Statistics
Probability
Mathematics
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