โ Scribed by Thomas A. Severini
No coin nor oath required. For personal study only.
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 provides an up-to-date account of recent results in the field, which has been undergoing rapid development.
1 Some Basic Concepts
2 Large-sample Approximations
3 Likelihood
4 First-order Asymptotic Theory
5 High-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 Set Used in the Examples
Statistics
๐ SIMILAR VOLUMES
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