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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Bing Li is Verne M. Wallaman Professor of Statistics at Pennsylvania State University. He is the author of Sufficient Dimension Reduction: Methods and Applications with R (2018). Dr. Li has served as an associate editor for The Annals of Statistics and is currently serving as an associate editor for Journal of the American Association.
G. Jogesh Babu is a distinguished professor of statistics, astronomy, and astrophysics, as well as director of the Center for Astrostatistics, at Pennsylvania State University. He was the 2018 winner of the Jerome Sacks Award for Cross-Disciplinary Research. He and his colleague Dr. E.D. Feigelson coined the term "astrostatistics," when they co-authored a book by the same name in 1996. Dr. Babu's numerous publications also include Statistical Challenges in Modern Astronomy V (with Feigelson, Springer 2012) and Modern Statistical Methods for Astronomy with R Applications (2012).Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Hardback. Etat : Good. has checkmarks in pencil on several chapters, the text is not obscured, Shows minor wear to the cover and pages. The binding is fully intact and secure. This is a good reading or studying copy and has been verified that all pages are legible. 100% satisfaction guaranteed! --- Part of the Springer Texts in Statistics series, this graduate textbook of theoretical statistics is organised around three principal themes: finite-sample theory, asymptotic theory, and Bayesian statistics. A chapter on estimating equations is included as a means of unifying methodologies such as generalized linear models, generalized estimating equations, quasi-likelihood estimation, and conditional inference, and a common set of regularity conditions and tools is applied throughout. The Bayesian material treats estimation, testing, and classification, developing the prior and posterior framework from Bayes' theorem. The authors suggest modular pathways through the material - for instance, Chapters 1, 3, 4, 7, 10, and 11 for an advanced course on hypothesis testing, or Chapters 1, 2, 5, part of 6, 7, 8, and 9 for a course on point estimation and Bayesian statistics, supporting use over one or two semesters. The text grew from lecture notes for two graduate courses taught at the Pennsylvania State University. Exercise sets are provided. N° de réf. du vendeur OA-251017-shelf-20-009
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