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A Graduate Course on Statistical Inference

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概要 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.続きを見る
目次 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.
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本文を見る Full text available from Springer Mathematics and Statistics eBooks 2019 English/International

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登録日 2020.06.27
更新日 2020.06.28