1.

Record Nr.

UNINA9910349347103321

Autore

Li Bing

Titolo

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

Pubbl/distr/stampa

New York, NY : , : Springer New York : , : Imprint : Springer, , 2019

ISBN

9781493997619

1493997610

Edizione

[1st ed. 2019.]

Descrizione fisica

1 online resource (XII, 379 p. 148 illus.)

Collana

Springer Texts in Statistics, , 2197-4136

Disciplina

519.5

Soggetti

Statistics

Statistical Theory and Methods

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

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.