A Graduate Course on Statistical Inference /
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...
| Main Authors: | , |
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| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
New York, NY :
Springer New York : Imprint: Springer,
2019.
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| Edition: | 1st ed. 2019. |
| Series: | Springer Texts in Statistics,
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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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| Physical Description: | 1 online resource (XII, 379 pages 148 illustrations) |
| ISBN: | 9781493997619 |
| ISSN: | 2197-4136 |
| DOI: | 10.1007/978-1-4939-9761-9 |