Statistical Inference: An Integrated Bayesian/Likelihood Approach (CRC Monographs on Statistics & Applied Probability (Hardcover)) - Softcover

Aitkin, Murray

Softcover

ISBN 10: 0367383942 ISBN 13: 9780367383947

Verlag: Routledge, 2019

Inhaltsangabe

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.

After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It presents Bayesian versions of one- and two-sample t-tests, along with the corresponding normal variance tests. The author then thoroughly discusses the use of the multinomial model and noninformative Dirichlet priors in "model-free" or nonparametric Bayesian survey analysis, before covering normal regression and analysis of variance. In the chapter on binomial and multinomial data, he gives alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for assessing parametric models and a discussion of two-level variance component models and finite mixtures.

Emphasizing the principles of Bayesian inference and Bayesian model comparison, this book develops a unique methodology for solving challenging inference problems. It also includes a concise review of the various approaches to inference.

Über diesen Titel

Über die Autorin bzw. den Autor

Murray Aitkin is an honorary professorial fellow in the Department of Mathematics and Statistics at the University of Melbourne in Australia.

Von der hinteren Coverseite

Filling a gap in current Bayesian theory, this book presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing. The book discusses nonparametric Bayesian survey analysis, gives alternatives to current frequentist nonparametric methods, and includes new goodness-of-fit methods for assessing parametric models. It also covers normal regression, analysis of variance, two-level variance component models, and finite mixtures.

Details zur Ausgabe

• Verlag: Routledge
• Erscheinungsdatum: 2019
• Sprache: Englisch
• ISBN 10: 0367383942
• ISBN 13: 9780367383947
• Einband: Taschenbuch
• Auflage: 1
• Anzahl der Seiten: 254
• Kontakt zum Hersteller: Taylor & Francis Verlag GmbH; GPSR@taylorandfrancis.com; ; Kaufingerstraße 24; München; 80331; Deutschland