Progress in 2023, Leo edition

Following Andrew, Aki, Jessica, and Charles, and based on Andrew’s proposal, I list my research contributions for 2023.

Published:

1. Egidi, L. (2023). Seconder of the vote of thanks to Narayanan, Kosmidis, and Dellaportas and contribution to the Discussion of ‘Flexible marked spatio-temporal point processes with applications to event sequences from association football’. Journal of the Royal Statistical Society Series C: Applied Statistics, 72(5), 1129.
2. Marzi, G., Balzano, M., Egidi, L., & Magrini, A. (2023). CLC Estimator: a tool for latent construct estimation via congeneric approaches in survey research. Multivariate Behavioral Research, 58(6), 1160-1164.
3. Egidi, L., Pauli, F., Torelli, N., & Zaccarin, S. (2023). Clustering spatial networks through latent mixture models. Journal of the Royal Statistical Society Series A: Statistics in Society, 186(1), 137-156.
4. Egidi, L., & Ntzoufras, I. (2023). Predictive Bayes factors. In SEAS IN. Book of short papers 2023 (pp. 929-934). Pearson.
5. Macrì Demartino, R., Egidi, L., & Torelli, N. (2023). Power priors elicitation through Bayes factors. In SEAS IN. Book of short papers 2023 (pp. 923-928). Pearson.

Preprints:

1. Consonni, G., & Egidi, L. (2023). Assessing replication success via skeptical mixture priors. arXiv preprint arXiv:2401.00257. Submitted.

Softwares:

    CLC estimator

• free and open-source app to estimate latent unidimensional constructs via congeneric approaches in survey research (Marzi et al., 2023)

   footBayes package (CRAN version 0.2.0)

• diagonal inflated bivariate Poisson model (Karlis and Ntzoufras, 2003)
• zero-inflated Skellam model (Karlis and Ntzoufras, 2009)

   pivmet package (CRAN version 0.5.0)

• sparse finite mixtures implementation (Fruhwirt-Schnatter and Malsiner-Walli, 2019)
• Stan implementation

 

I hope and guess that the paper dealing with the replication crisis, “Assessing replication success via skeptical mixture priors” with Guido Consonni, could have good potential in the Bayesian assesment of replication success in social and hard sciences; this paper can be seen as an extension of the paper written by Leonhard Held and Samuel Pawel entitled “The Sceptical Bayes Factor for the Assessment of Replication Success“.  Moreover, I am glad that the paper “Clustering spatial networks through latent mixture models“, focused on a model-based clustering approach defined in a hybrid latent space, has been finally published in JRSS A.

Regarding softwares, the footBayes package, a tool to fit the most well-known soccer (football) models through Stan and maximum likelihood methods, has been deeply developed and enriched with new functionalities (2024 objective: incorporate CmdStan with VI/Pathfinder algorithms and write a package’s paper in JSS/R Journal format).