2023

1. COST

   A flexible two-piece normal dynamic linear model

   Emanuele Aliverti, Reinaldo B. Arellano-Valle, Fereshteh Kharari and Bruno Scarpa

   Computational Statistics, 2023

   Link Bib Cit

   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 B. and Kharari, Fereshteh and Scarpa, Bruno},
     year = {2023},
     journal = {Computational Statistics},
     doi = {doi.org/10.1007/s00180-023-01355-3},
   }

2. SPL

   Conjugate priors and bias reduction for logistic regression models

   Tommaso Rigon and Emanuele Aliverti

   Statistics and Probability Letters, 2023

   Link arXiv Bib Cit Code

   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},
   }

2022

1. SIM

   Dynamic modeling of the Italians’ attitude towards Covid-19

   Emanuele Aliverti and Massimiliano Russo

   Statistics in Medicine, 2022

   Link arXiv Bib Cit

   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},
   }

2. JRSS-A

   Dynamic modelling of mortality via mixtures of skewed distribution functions

   Emanuele Aliverti, Stefano Mazzuco and Bruno Scarpa

   Journal of the Royal Statistical Society: Series A (Statistics in Society), 2022

   Link arXiv Bib Cit

   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},
   }

3. SMA

   When does morbidity start? An analysis of changes in morbidity between 2013 and 2019 in Italy

   Andrea Pastore, Stefano Tonellato, Emanuele Aliverti and Stefano Campostrini

   Statistical Methods & Applications, 2022

   Link Bib Cit

   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},
   }

4. JCGS

   Stratified Stochastic Variational Inference for High-Dimensional Network Factor Model

   Emanuele Aliverti and Massimiliano Russo

   Journal of Computational and Graphical Statistics, 2022

   Link arXiv Bib Cit Code

   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},
   }

5. AOAS

   Composite mixture of log-linear models with application to psychiatric studies

   Emanuele Aliverti and David B. Dunson

   The Annals of Applied Statistics, 2022

   Link arXiv Bib Cit

   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},
   }

2021

1. JRSS-A

   Removing the influence of group variables in high-dimensional predictive modelling

   Emanuele Aliverti, Kristian Lum, James E. Johndrow and David B. Dunson

   Journal of the Royal Statistical Society: Series A (Statistics in Society), 2021

   Link arXiv Bib Cit

   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},
   }

2020

1. BIOINF

   Projected t-SNE for batch correction

   Emanuele Aliverti, Jeffrey L. Tilson, Dayne L. Filer, Benjamin Babcock, Alejandro Colaneri, Jennifer Ocasio, Timothy R. Gershon, Kirk C. Wilhelmsen and David B. Dunson

   Bioinformatics, 2020

   Link arXiv Bib Cit Code

   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},
   }

2019

1. SAM

   Spatial modeling of brain connectivity data via latent distance models with nodes clustering

   Emanuele Aliverti and Daniele Durante

   Statistical Analysis and Data Mining: The ASA Data Science Journal, 2019

   Link Bib Cit

   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},
   }