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.
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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.
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Hardback. Condition: Good. has checkmarks in pencil on several chapters, the text is not obscured, Shows minor wear to the cover and pages. The binding is fully intact and secure. This is a good reading or studying copy and has been verified that all pages are legible. 100% satisfaction guaranteed! --- A graduate-level treatment of statistical estimation and hypothesis testing by Bing Li and G. Jogesh Babu, published by Springer in 2019 as part of the Springer Texts in Statistics series. The volume integrates finite-sample results, asymptotic methods, and Bayesian approaches, applying a consistent framework of assumptions and analytical tools across topics. A dedicated chapter on estimating equations brings together related techniques such as generalized linear models, quasi-likelihood, generalized estimating equations, and conditional inference. The text is intended for doctoral students and researchers seeking a unified foundation in modern inferential theory. Seller Inventory # OA-251017-shelf-20-009
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