Handbook on Bayesian, Fiducial and Frequentist (BFF) Inferences (forthcoming by Chapman & Hall; co-editors: James Berger, Xiao-Li Meng, Nancy Reid and Min-ge Xie)
Despite the tremendous progress in recent years, statistical science is still a young discipline and continues to have several different and competing paths in its approaches and to its foundations. The surge of interest in foundational research of statistical inference has resulted in numerous publications, and recent vibrant research activities across multi-disciplines of statistics, philosophy and other science fields have highlighted the importance of the development. The BFF development not only bridges foundations for scientific learning, but also facilitates objective and replicable scientific research, and provides scalable computing methodologies for the analysis of big data. This new Handbook on Bayesian, Fiducial and Frequentist (BFF) Inferences is targeted to provide a coherent introduction and overview for a general audience the most recent developments on modern inferences across BFF perspectives. The Handbook is primarily aimed at
- at researchers who already work on foundations of statistical inference but desire a broader, more structured and stimulating overview,
- at researchers and students entering the field of statistical science to give them orientation, as a reference book for general BFF inference and data analysis practitioners,
- at scientists in other fields who work on or apply cutting edge statistical inference tools and are interested in acquiring a more thorough knowledge of modern inferential methods.
The articles should appeal to readers across a wide range so should be slightly more introductory than a journal article but still represent cutting-edge knowledge of the topic at hand.
For more information or if you have any suggestions, please contact one of the co-editors or send an email to email@example.com.