UID:
almahu_9949708259202882
Format:
1 online resource.
Edition:
First edition.
ISBN:
9780429341731
,
0429341733
,
9781003837695
,
1003837697
,
9781003837640
,
1003837646
Series Statement:
Chapman & Hall/CRC handbooks of modern statistical methods
Content:
"The emergence of data science, in recent decades, has magnified the need for efficient methodology for analyzing data and highlighted the importance of statistical inference. Despite the tremendous progress that has been made, statistical science is still a young discipline and continues to have several different and competing paths in its approaches and its foundations. While the emergence of competing approaches is a natural progression of any scientific discipline, differences in the foundations of statistical inference can sometimes lead to different interpretations and conclusions from the same dataset. The increased interest in the foundations of statistical inference has led to many publications, and recent vibrant research activities in statistics, applied mathematics, philosophy and other fields of science reflect the importance of this development. The BFF approaches not only bridge foundations and scientific learning, but also facilitate objective and replicable scientific research, and provide scalable computing methodologies for the analysis of big data. Most of the published work typically focusses on a single topic or theme, and the body of work is scattered in different journals. This handbook provides a comprehensive introduction and broad overview of the key developments in the BFF schools of inference. It is intended for researchers and students who wish for an overview of foundations of inference from the BFF perspective and provides a general reference for BFF inference"--
Note:
1. Risky Business Stephen Stigler2. Empirical Bayes: Concepts and Methods Bradley Efron3. Distributions for Parameters Nancy Reid4. Objective Bayesian Inference and its Relationship to Frequentism James Berger, Jose Bernardo and Dongchu Sun5. Fiducial Inference, Then and Now Alexander Philip Dawid6. Bridging Bayesian, frequentist and fiducial inferences using confidence distributions Suzanne Thornton and Min-ge Xie7. Objective Bayesian Testing and Model Uncertainty James Berger, Gonzalo García-Donato, Elias Moreno and Luis Pericchi8. "A BFFer's Exploration with Nuisance Constructs: Bayesian p-value, H likelihood, and Cauchyanity" Xiao-Li Meng9. Bayesian neural networks and dimensionality reduction Deborshee Sen, Theodore Papamarkou and David Dunson10. The Tangent Exponential Model Anthony Davison and Nancy Reid11. Data Integration and Model Fusion in the Bayesian and Frequentist Frameworks Emily C. Hector, Lu Tang, Ling Zhou and Peter X.K. Song12. How the game-theoretic foundation for probability resolves the Bayesian vs. frequentist standoff Glenn Shafer13. "Introduction to Generalized Fiducial Inference" Alexander Murph, Jan Hannig and Jonathan P. Williams 14. "Dempster-Shafer Theory for Statistical Inference" Ruobin Gong15. Slicing and Dicing a Path Through the Fiducial Forest Joseph B. Lang16. Inferential models and possibility measures Chuanhai Liu and Ryan Martin17. Conformal predictive distributions: an approach to nonparametric ducial prediction Vladimir Vovk18. Fiducial Inference and Decision Theory Gunnar Taraldsen and Bo Henry LindquistIndex
Additional Edition:
Print version: Handbook of Bayesian, fiducial, and frequentist inference Boca Raton, FL : CRC Press, 2024 ISBN 9780367321987
Language:
English
DOI:
10.1201/9780429341731
URL:
https://www.taylorfrancis.com/books/9780429341731
