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＜E-Book＞
A Graduate Course on Statistical Inference / by Bing Li, G. Jogesh Babu
(Springer Texts in Statistics. ISSN:21974136)

Edition	1st ed. 2019.
Publisher	(New York, NY : Springer New York : Imprint: Springer)
Year	2019
Size	XII, 379 p. 148 illus : online resource
Authors	*Li, Bing author Babu, G. Jogesh author SpringerLink (Online service)
Subjects	LCSH:Statistics  FREE:Statistical Theory and Methods
Notes	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 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 HTTP:URL=https://doi.org/10.1007/978-1-4939-9761-9

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