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Record Nr. |
UNINA9910349347103321 |
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Autore |
Li Bing |
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Titolo |
A Graduate Course on Statistical Inference / / by Bing Li, G. Jogesh Babu |
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Pubbl/distr/stampa |
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New York, NY : , : Springer New York : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XII, 379 p. 148 illus.) |
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Collana |
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Springer Texts in Statistics, , 2197-4136 |
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Disciplina |
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Soggetti |
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Statistics |
Statistical Theory and Methods |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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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Sommario/riassunto |
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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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