Andreella, A., Aliverti, E., Caldura, F., and Campostrini, S. 2024. Spatial clusters for demand and supply of early childhood education and care services in Italy. Socio-Economic Planning Sciences, 95
@article{andreella2024,author={Andreella, Angela and Aliverti, Emanuele and Caldura, Federico and Campostrini, Stefano},journal={Socio-Economic Planning Sciences},pages={102034},year={2024},publisher={Elsevier},doi={10.1016/j.seps.2024.102034}}
Aliverti, E., Arellano-Valle, R.B., Kahrari, F. & Scarpa, B. (2023). A flexible two-piece normal dynamic linear model. Computational Statistics
@article{arellano23,title={A flexible two-piece normal dynamic linear model},author={Aliverti, Emanuele and Arellano-Valle, Reinaldo Boris and Kahrari, Fereshteh and Scarpa, Bruno},journal={Computational Statistics},volume={38},number={4},pages={2075--2096},year={2023},publisher={Springer},}
Rigon, T., & Aliverti, E. (2023). Conjugate priors and bias reduction for logistic regression models. Statistics & Probability Letters, 202, 109901.
@article{Rigon-bias23,title={Conjugate priors and bias reduction for logistic regression models},author={Rigon, Tommaso and Aliverti, Emanuele},journal={Statistics and Probability Letters},doi={doi.org/10.1016/j.spl.2023.109901},year={2023},}
Aliverti, E, Russo, M. (2022). Dynamic modeling of the Italians’ attitude towards Covid-19. Statistics in Medicine. 41 (26): 5189– 5202
@article{Aliverti-covid22,title={Dynamic modeling of the Italians' attitude towards Covid-19},author={Aliverti, Emanuele and Russo, Massimiliano},journal={Statistics in Medicine},year={2022},volume={41},number={26},pages={5189-5202},doi={https://doi.org/10.1002/sim.9560},}
Aliverti, E., Mazzuco, S. & Scarpa, B. (2022) Dynamic modelling of mortality via mixtures of skewed distribution functions. Journal of the Royal Statistical Society: Series A, 185: 1030–1048
@article{Aliverti-dysm22,author={Aliverti, Emanuele and Mazzuco, Stefano and Scarpa, Bruno},title={Dynamic modelling of mortality via mixtures of skewed distribution functions},journal={Journal of the Royal Statistical Society: Series A (Statistics in Society)},volume={185},number={3},pages={1030-1048},keywords={Bayesian inference, dynamic modelling, mixture model, skew-normal distribution},doi={https://doi.org/10.1111/rssa.12808},year={2022},}
Pastore, A., Tonellato, S.F., Aliverti, E. and Campostrini. S. (2022) When does morbidity start? An analysis of changes in morbidity between 2013 and 2019 in Italy. Stat Methods Appl
@article{Pastore-passi22,title={When does morbidity start? An analysis of changes in morbidity between 2013 and 2019 in Italy},author={Pastore, Andrea and Tonellato, Stefano and Aliverti, Emanuele and Campostrini, Stefano},journal={Statistical Methods \& Applications},year={2022},volume={(in press)},doi={https://doi.org/10.1007/s10260-022-00668-9},}
Aliverti, E. & Russo, M. (2022) Stratified Stochastic Variational Inference for High-Dimensional Network Factor Model, Journal of Computational and Graphical Statistics, 31:2, 502-511
@article{aliverti-svilf22,author={Aliverti, Emanuele and Russo, Massimiliano},title={Stratified Stochastic Variational Inference for High-Dimensional Network Factor Model},journal={Journal of Computational and Graphical Statistics},volume={31},number={2},pages={502-511},year={2022},publisher={Taylor & Francis},doi={10.1080/10618600.2021.1984929},url={https://doi.org/10.1080/10618600.2021.1984929},}
Aliverti, E. and Dunson, D. B. (2022) "Composite mixture of log-linear models with application to psychiatric studies." Ann. Appl. Stat. 16 (2) 765 - 790
@article{Aliverti-mills22,author={Aliverti, Emanuele and Dunson, David B.},title={{Composite mixture of log-linear models with application to psychiatric studies}},volume={16},journal={The Annals of Applied Statistics},number={2},publisher={Institute of Mathematical Statistics},pages={765 -- 790},keywords={Bayesian modelling, categorical data, Contingency table, log-linear models, mixture model, Psychiatric profiles},year={2022},doi={10.1214/21-AOAS1515},url={https://doi.org/10.1214/21-AOAS1515},}
Aliverti, E., Lum, K., Johndrow, J.E. and Dunson, D.B. (2021) Removing the influence of group variables in high-dimensional predictive modelling. Journal of the Royal Statistical Society: Series A, 184: 791-811
@article{Aliverti-sog21,author={Aliverti, Emanuele and Lum, Kristian and Johndrow, James E. and Dunson, David B.},title={Removing the influence of group variables in high-dimensional predictive modelling},journal={Journal of the Royal Statistical Society: Series A (Statistics in Society)},volume={184},number={3},pages={791-811},keywords={batch effects, constrained optimization, criminal justice, neuroscience, orthogonal predictions, predictive modelling, singular value decomposition},doi={https://doi.org/10.1111/rssa.12613},url={https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssa.12613},year={2021},}
Emanuele Aliverti, Jeffrey L. Tilson, Dayne L. Filer, Benjamin Babcock, Alejandro Colaneri, Jennifer Ocasio, Timothy R. Gershon, Kirk C. Wilhelmsen and David B. Dunson
Emanuele Aliverti, Jeffrey L Tilson, Dayne L Filer, Benjamin Babcock, Alejandro Colaneri, Jennifer Ocasio, Timothy R Gershon, Kirk C Wilhelmsen, David B Dunson (2020) Projected t-SNE for batch correction, Bioinformatics, Volume 36, Issue 11, Pages 3522–3527
@article{Aliverti-tsne20,author={Aliverti, Emanuele and Tilson, Jeffrey L. and Filer, Dayne L. and Babcock, Benjamin and Colaneri, Alejandro and Ocasio, Jennifer and Gershon, Timothy R. and Wilhelmsen, Kirk C. and Dunson, David B.},title={{Projected t-SNE for batch correction}},journal={Bioinformatics},volume={36},number={11},pages={3522-3527},year={2020},issn={1367-4803},doi={10.1093/bioinformatics/btaa189},}
Aliverti, E, Durante, D. (2019) Spatial modeling of brain connectivity data via latent distance models with nodes clustering. Stat Anal Data Min: The ASA Data Sci Journal. 12: 185–196
@article{Aliverti-brain19,author={Aliverti, Emanuele and Durante, Daniele},title={Spatial modeling of brain connectivity data via latent distance models with nodes clustering},journal={Statistical Analysis and Data Mining: The ASA Data Science Journal},volume={12},number={3},pages={185-196},keywords={latent space model, mixture of Gaussians prior, spatial effect, structural brain network},doi={https://doi.org/10.1002/sam.11412},url={https://onlinelibrary.wiley.com/doi/abs/10.1002/sam.11412},year={2019},}