| Dewey Class |
519.5 |
| Title |
A Graduate Course on Statistical Inference ([EBook]) / by Bing Li, G. Jogesh Babu. |
| Author |
Li, Bing |
| Added Personal Name |
Babu, G. Jogesh |
| Other name(s) |
SpringerLink (Online service) |
| Publication |
New York, NY : Springer , 2019. |
| Physical Details |
XII, 379 pages, 148 illus. : online resource. |
| Series |
Springer Texts in Statistics |
| ISBN |
9781493997619 |
| Summary Note |
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.: |
| Contents note |
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. |
| System details note |
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| Internet Site |
https://doi.org/10.1007/978-1-4939-9761-9 |
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