Advances in Statistical Inference: Bayesian and likelihood interplay
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Advances in Statistical Inference: Bayesian and likelihood interplay Fulvio De Santis · Laura Ventura
Published online: 1 July 2014 © Sapienza Universitá di Roma 2014
This volume has been inspired by the aim of gathering a number of theoretical papers that, though from different schools of inference—frequentist and Bayesian, share the same interest towards studying complex statistical models. Indeed, both frequentist and Bayesian inference can entail various drawbacks, due to the complexity or misspecification of the model, or to the presence of many nuisance parameters. These difficulties may be overcome from a theoretical point of view through approximate likelihoods and scoring rules, and from a computational point of view through higher-order asymptotic expansions or computational tools from Bayesian inference. Specifically, the volume focuses on the interplay between Bayesian and frequentist inference in addressing these problems. The goal is to highlight and to disseminate some of the topics emerged in the international Workshop Recent advances in Statistical Inference: Theory and case studies, held in Padova, Italy, March 21–23, 2013. The Workshop has been a forum for the exchange of new ideas and thoughts on recent and ongoing research in both Bayesian methodology and modern likelihood inference. Although the topics covered in this volume are diverse, similar themes recur, as research is mostly fueled by the need to deal with complex models, for which traditional methods do not provide viable solutions. Among the topics presented we have Bayesian/frequentist challenges for categorical data analysis, Bayesian-frequentist sample size determination, integrated likelihood inference in semi parametric regression models, approximate Bayesian computation with modified log-likelihood ratios, theory and applications of proper scoring rules, quasi likelihood approximation of posterior distributions for likelihood-intractable complex models, mitigating multicollinearity with spike-and-slab priors, empirical Bayes methods in classical and Bayesian inference. While not exhaustive, this list should give a feeling of the issues discussed at the Workshop.
F. De Santis (B) Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy e-mail: [email protected] L. Ventura Department of Statistical Sciences, University of Padua, Padua, Italy
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F. De Santis, L. Ventura
The relationships between Bayesian and likelihood methods have a long history. An important occasion for the merging of ideas from the two inferential approaches has been represented by the series of Workshop on Objective Bayesian Methodology, started in 1996 and held, since then, in the USA, Europe and, more recently, in Asia. The seminal workshop was organized by Jim Berger and held in Purdue University. The title of that conference was International Meeting on Default Bayesian Methodology. Just one year before, in June 1995, more or less the same researchers met (always in Purdue) for the Workshop on Intrinsic Bayes factors
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