A Graduate Course on Statistical Inference / / by Bing Li, G. Jogesh Babu

• Autore: Li Bing
• Pubblicazione: New York, NY : , : Springer New York : , : Imprint : Springer, , 2019
• Edizione: 1st ed. 2019.
• Descrizione fisica: 1 online resource (XII, 379 p. 148 illus.)
• Disciplina: 519.5
• Soggetto topico: Statistics; Statistical Theory and Methods
• Persona (resp. second.): BabuG. Jogesh
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: A Graduate Course on Statistical Inference
• ISBN: 9781493997619; 1493997610
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910349347103321
• Lo trovi qui: Univ. Federico II
• Serie: Springer Texts in Statistics, . 2197-4136