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A graduate course on statistical inference

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A graduate course on statistical inference/ by Bing Li, G. Jogesh Babu.
Author:	Li, Bing.
other author:	Babu, G. Jogesh.
Published:	New York, NY :Springer New York : : 2019.,
Description:	xii, 379 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Mathematical statistics. -
Online resource:	https://doi.org/10.1007/978-1-4939-9761-9
ISBN:	9781493997619

A graduate course on statistical inference
Li, Bing.

A graduate course on statistical inference[electronic resource] /by Bing Li, G. Jogesh Babu. - New York, NY :Springer New York :2019. - xii, 379 p. :ill., digital ;24 cm. - Springer texts in statistics,1431-875X. - Springer texts in statistics..

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.

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.

ISBN: 9781493997619

Standard No.: 10.1007/978-1-4939-9761-9doiSubjects--Topical Terms:

527941
Mathematical statistics.

LC Class. No.: QA276 / .L535 2019

Dewey Class. No.: 519.54

A graduate course on statistical inference
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505 0 $a 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.
520 $a 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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