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.
Author(s): Bing Li, G. Jogesh Babu
Series: Springer Texts In Statistics
Publisher: Springer
Year: 2019
Language: English
Pages: 386
Tags: Statistical Inference, Graduate Course
Front Matter ....Pages I-XII
Probability and Random Variables (Bing Li, G. Jogesh Babu)....Pages 1-29
Classical Theory of Estimation (Bing Li, G. Jogesh Babu)....Pages 31-60
Testing Hypotheses for a Single Parameter (Bing Li, G. Jogesh Babu)....Pages 61-98
Testing Hypotheses in the Presence of Nuisance Parameters (Bing Li, G. Jogesh Babu)....Pages 99-134
Basic Ideas of Bayesian Methods (Bing Li, G. Jogesh Babu)....Pages 135-172
Bayesian Inference (Bing Li, G. Jogesh Babu)....Pages 173-201
Asymptotic tools and projections (Bing Li, G. Jogesh Babu)....Pages 203-236
Asymptotic theory for Maximum Likelihood Estimation (Bing Li, G. Jogesh Babu)....Pages 237-259
Estimating equations (Bing Li, G. Jogesh Babu)....Pages 261-293
Convolution Theorem and Asymptotic Efficiency (Bing Li, G. Jogesh Babu)....Pages 295-327
Asymptotic Hypothesis Test (Bing Li, G. Jogesh Babu)....Pages 329-374
Back Matter ....Pages 375-379
