Agenda

Speaker: Leonardo Egidi, University of Trieste, DEAMS

Link Zoom: https://unive.zoom.us/j/85153268624?pwd=MzBhdlA2M1B2dThJQ2Y5T0EwUE5PZz09
Meeting ID: 851 5326 8624
Passcode: SanMarco2

Abstract:
Nowadays a Bayesian model needs to be reproducible, generative, predictive, robust, computationally scalable, and able to provide sound inferential conclusions. In this wide 'Bayes New World', Bayes factors still represent one of the most well-known and commonly adopted tools to perform model selection and hypothesis testing; however, they are usually criticized due to their intrinsic lack of calibration, and they are rarely used to measure the predictive accuracy arising from competing models.
Perhaps we feel that in this framework Bayes factors (BFs) are still alive and propose two distinct approaches relying on BFs from our most recent research.
Regarding the analysis of replication studies within the so-called crisis of scientific replication (Held, 2020), we follow the stream outlined by Pawel and Held (2022) and propose a skeptical mixture prior which represents the prior of an investigator who is unconvinced by the original findings. Its novelty lies in the fact that it incorporates skepticism while controlling for prior-data conflict (Egidi et al., 2021). Consistency properties of the resulting skeptical BF are provided together with a thorough analysis of the main features of our proposal (Consonni and Egidi, 2023; Macrì Demartino et al., 2024a).
Then, with regard to prediction, we propose a new algorithmic protocol to transform Bayes factors into measures that evaluate the pure and intrinsic predictive capabilities of models in terms of posterior predictive distributions, by assessing some preliminary theoretical properties and investigating how properly using these tools in clinical trials and in other biostatistics' scenarios (Macrì Demartino et al., 2024b).

Short Bibliography:

• Egidi, L., Pauli, F., & Torelli, N. (2022). Avoiding prior–data conflict in regression models via mixture priors. Canadian Journal of Statistics, 50(2), 491-510.
• Egidi L. & Consonni G. (2023) Assessing replication success via skeptical mixture priors. https://arxiv.org/abs/2401.00257
• Held, L. (2020). A new standard for the analysis and design of replication studies. Journal of the Royal Statistical Society Series A: Statistics in Society, 183(2), 431-448.
• Macrì Demartino, R., Egidi, Pawel, S., L., Held, L. (2024a) Mixture priors for replication studies. https://arxiv.org/abs/2406.19152
• Macrì Demartino, R., Egidi, L., Torelli, N. & Ntzoufras, I. (2024b). Eliciting prior information from clinical trials via calibrated Bayes factor. https://arxiv.org/abs/2406.19346
• Pawel, S., & Held, L. (2022). The sceptical Bayes factor for the assessment of replication success. Journal of the Royal Statistical Society Series B: Statistical Methodology, 84(3), 879-911.

Bio Sketch:
Leonardo Egidi is an assistant professor (rtd-b) of Statistics at the Department of Business, Economics, Mathematics and Statistics "Bruno de Finetti" (DEAMS) of the University of Trieste.
Previously, he was a postdoctoral research fellow at DEAMS (2018-2020). He obtained a PhD in Statistics from the Department of Statistical Sciences, University of Padova in 2018. During his PhD studies he was a visiting scholar in the Department of Statistical Sciences of Columbia University, New York under the supervision of Andrew Gelman.