A Graduate Course on Statistical Inference
A Graduate Course on Statistical Inference by Bing Li is a comprehensive textbook published by Springer New York on August 2, 2019. This 1st edition, consisting of 379 pages, is written in English and provides an accessible overview of statistical estimation and inference, reflecting current trends in statistical research.
Readers will find that the book focuses on three main themes: finite-sample theory, asymptotic theory, and Bayesian statistics. It includes a chapter on estimating equations that unifies various methodologies, such as generalized linear models and quasi-likelihood estimation. By employing a standardized set of assumptions and tools, the text aims for coherence and cohesion, making it suitable for graduate-level courses that can be completed in one or two semesters.
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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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