Description
This textbook offers an accessible and comprehensive overview of statistical estimation and inference that reflects current trends in statistical research. It draws from three main themes throughout: the finite-sample theory, the asymptotic theory, and Bayesian statistics. The authors have included a chapter on estimating equations as a means to unify a range of useful methodologies, including generalized linear models, generalized estimation equations, quasi-likelihood estimation, and conditional inference. They also utilize a standardized set of assumptions and tools throughout, imposing regular conditions and resulting in a more coherent and cohesive volume. Written for the graduate-level audience, this text can be used in a one-semester or two-semester course.
Table of Contents
1. Probability and Random Variables.- 2. Classical Theory of Estimation.- 3. Testing Hypotheses in the Presence of Nuisance Parameters.- 4. Testing Hypotheses in the Presence of Nuisance Parameters.- 5. Basic Ideas of Bayesian Methods.- 6. Bayesian Inference.- 7. Asymptotic Tools and Projections.- 8. Asymptotic Theory for Maximum Likelihood Estimation.- 9. Estimating Equations.- 10. Convolution Theorem and Asymptotic Efficiency.- 11. Asymptotic Hypothesis Test.- References.- Index.
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