Image of
In this section

Publications

Selected publications

2023

  • Anceschi, N., Fasano, A., Durante, D., Zanella, G. (2023). Bayesian conjugacy in probit, tobit, multinomial probit and extensions: A review and new results. Journal of the American Statistical Association, 118, 1451–1469.
  • Ascolani, F., Franzolini, B., Lijoi, A., Prünster, I. (2023). Nonparametric priors with full-range borrowing of information. Biometrika, forthcoming.
  • Ascolani, F., Lijoi, A., Rebaudo, G. and Zanella, G. (2023). Clustering consistency with Dirichlet process mixtures. Biometrika, 110, 551-558.
  • Ascolani, F., Lijoi, A., Ruggiero, M. (2023). Smoothing distributions for conditional Fleming-Viot and Dawson-Watanabe diffusions. Bernoulli, 29, 1410-1434.
  • Catalano, M., Lavenant, H., Lijoi, A., Prünster, I. (2023). A Wasserstein index of dependence for random measures. Journal of the American Statistical Association, forthcoming.
  • Davison A. C., Padoan, S. A., Stufler, G. (2023). Tail risk inference via expectiles in heavy-tailed time series. Journal of Business & Economic Statistics, 41, 876-889.
  • Franzolini, B., Lijoi, A., Prünster, I. (2023). Model Selection for Maternal Hypertensive Disorders with Symmetric Hierarchical Dirichlet Processes. The Annals of Applied Statistics, 17, 313-332.
  • Gagnon P., Maire F., Zanella G. (2023). Improving multiple-try Metropolis with local balancing. Journal of Machine Learning Research, 24, 1-59.
  • Lijoi, A., Prünster, I., Rigon, T. (2023). Finite-dimensional discrete random structure and Bayesian clustering. Journal of the American Statistical Association, forthcoming.
  • Lijoi, A., Prünster, I., Rebaudo, G. (2023). Flexible clustering via hidden hierarchical Dirichlet priors. Scandinavian Journal of Statistics, 50, 213-234.
  • Sariev, H., Fortini, S., Petrone, S. (2023). Infinite-color randomly reinforced urns with dominant colors. Bernoulli, 29, 132-152.
  • Silva L. and Zanella G. (2023). Robust leave-one-out cross-validation for high-dimensional Bayesian models. Journal of the American Statistical Association, forthcoming.
  • Szabo, B.T., Vuursteen, L., van Zanten, J.H. (2023). Optimal high-dimensional and nonparametric distributed testing under communication constraints. The Annals of Statistics, forthcoming.
  • Vogrinc J., Livingstone S., Zanella G. (2023). Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms. Biometrika, 110, 579-595.

2022

  • Alexopoulos, A., Dellaportas, P. and Papaspiliopoulos, O. (2022). Bayesian prediction of jumps in large panels of time series, Bayesian Analysis, 17, 651–683.
  • Betancourt B., Zanella G. and Steorts R. (2022). Random Partition Models for Microclustering Tasks. Journal of the American Statistical Association, 117, 1215-1227.
  • Bonsaglio, M., Fortini, S., Ventz, S., Trippa, L. (2022). Approximating the Operating Characteristics of Bayesian Uncertainty Directed Trial Designs. Journal of Statistical Planning and Inference, 221, 90-99.
  • Cao, J., Durante, D., Genton, M. (2022). Scalable computation of predictive probabilities in probit models with Gaussian process priors. Journal of Computational and Graphical Statistics, 31, 709–720.
  • Catalano, M., De Blasi, P., Lijoi, A., Prünster, I. (2022). Posterior asymptotics for boosted hierarchical Dirichlet process mixtures. Journal of Machine Learning Research, 23, 1-23.
  • De Blasi, P., Mena, R.H., Prünster, I. (2022). Asymptotic behavior of the number of distinct values in a sample from the geometric stick-breaking process. Annals of the Institute of Statistical Mathematics, 74, 143-165.
  • Fasano, A., and Durante, D. (2022). A class of conjugate priors for multinomial probit models which includes the multivariate normal one. Journal of Machine Learning Research, 23, 1–26.
  • Fasano, A., Durante, D., Zanella, G. (2022). Scalable and accurate variational Bayes for high–dimensional binary regression models. Biometrika, 109, 901–919.
  • Legramanti S., Rigon, T., Durante, D., Dunson, D.B. (2022). Extended stochastic block models with application to criminal networks. Annals of Applied Statistics, 16, 2369–2395.
  • Livingstone S. and Zanella G. (2022). The Barker proposal: combining robustness and efficiency in gradient-based MCMC. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 84, 496-523.
  • Nieman, D. Szabo, B.T., van Zanten, J.H. (2022). Contraction rates for sparse variational approximations in Gaussian process regression. Journal of Machine Learning Research, 23, 1-26.
  • Padoan, S. A. and Rizzelli, S. (2022). Strong consistency of nonparametric Bayesian inferential methods for multivariate max-Stable distributions. The Annals of Statistics, 50, 1490-1518.
  • Padoan, S. A. and Stufler, G. (2022). Joint inference on extreme expectiles for multivariate heavy-tailed distributions. Bernoulli, 28, 1021-1048.
  • Ray, K. and Szabo, B. (2022). Variational Bayes for high-dimensional linear regression with sparse priors. Journal of the American Statistical Association, 117, 1270-1281.
  • Szabo, B.T., Vuursteen, L., van Zanten, J.H. (2022). Optimal distributed composite testing in high-dimensional Gaussian models with 1-bit communication. IEEE Transactions on Information Theory, 68, 4070-4084.

2021

  •  Ascolani, F., Lijoi, A. and Ruggiero, M. (2021). Predictive inference with Fleming-Viot driven dependent Dirichlet processes. Bayesian Analysis, 16, 371-395.
  • Baldassi, C., Cerreia-Vioglio, S., Maccheroni, F., Marinacci, M., and Pirazzini, M. (2020). A Behavioral characterization of the drift diffusion model and its multialternative extension for choice under time pressure. Management Science, forthcoming.
  • Becchetti, L., Clementi, A., Pasquale, F., Trevisan, L. and Ziccardi,I. (2021). Expansion and Flooding in Dynamic Random Networks with Node Churn. Proc. of ICDCS 2021. 
  • Beranger, B., Padoan, S. A., Sisson, S. A. (2021). Estimation and uncertainty quantification for extreme quantile regions. Extremes, forthcoming.
  • Berger, L., Marinacci, M., Berger, N., Bosetti, V., Gilboa, I., Hansen, L. P., Jarvis, C., Smith, R. D. (2021). Rational policymaking during a pandemic. Proceedings of the National Academy of Sciences of the United States of America, 118, 4.
  • Betancourt, B., Zanella, G. and Steorts, R. (2021). Random Partition Models for Microclustering Tasks. Journal of the American Statistical Association, forthcoming.
  • Borgonovo, E., Gordon B., H., Victor Richmond R. J., Plischke, E. (2021). Probabilistic sensitivity measures as information value. European Journal of Operational Research, 289, 595-610.
  •  Borgonovo, E., Plischke, E., Rabitti, G. (2021). Interactions and computer experiments. Scandinavian Journal of Statistics, 1-30, forthcoming. 
  • Camerlenghi, F., Lijoi, A., Prünster, I. (2021). Survival analysis via hierarchically dependent mixture hazards. The Annals of Statistics, 49, 863-884. 
  • Cao, J., Durante, D., Genton, M.G. (2021). Scalable computation of predictive probabilities in probit models with Gaussian process priors. Journal of Computational and Graphical Statistics, forthcoming. 
  • Catalano, M., Antonio L., Prünster, I. (2021). Measuring dependence in the Wasserstein distance for Bayesian nonparametric models. Annals of Statistics, 49, 2916 - 2947.
  • Chen, A., Shi, J., Trevisan, L. (2021). Cut Sparsification of the Clique Beyond the Ramanujan Bound. Proc. of SODA 2022, forthcoming.
  • Denti, F., Guindani, M., Leisen, F., Lijoi, A., Vannucci, M. and Wadsworth, D. (2021). Two-group Poisson-Dirichlet mixtures for multiple testing. Biometrics, 77, 622-633.
  • Fasano, A., Durante, D. (2021). A class of conjugate priors for multinomial probit models which includes the multivariate normal one. Journal of Machine Learning Research, forthcoming.
  • Fasano, A., Durante, D., Rebaudo, G., Petrone, S. (2021). A closed-form filter for binary time series. Statistics and Computing, 31, 47
  • Fasano, A., Durante, D., Zanella, G. (2021). Scalable and accurate variational Bayes for high-dimensional binary regression models. Biometrika, forthcoming.
  • Hashorva, E., Padoan S., Rizzelli, S. (2021). Multivariate extremes over a random number of observations. Scandinavian Journal of Statistics, forthcoming.
  • Legramanti, S., Rigon, T., Durante, D., Dunson, D.B. (2021). Extended stochastic block models with application to criminal networks. Annals of Applied Statistics, forthcoming. 
  • Livingstone, S., Zanella, G. (2021). The Barker proposal: combining robustness and efficiency in gradient-based MCMC. Journal of the Royal Statistical Society Series B Statistical Methodology, forthcoming.
  • Lijoi, A., Prünster, I., Rebaudo, G. (2021). Flexible clustering via hidden hierarchical Dirichlet priors. Scandinavian Journal of Statistics, forthcoming. 
  • Lu, X., Borgonovo, E. (2021). Global sensitivity analysis in epidemiological modeling. European Journal of Operational Research, forthcoming.
  • Padoan, S. A., Stupfler, G. (2021). Joint inference on extreme expectiles for multivariate heavy-tailed distributions. Bernoulli, forthcoming.
  • Padoan, S.A., Rizzelli, S. (2021). Consistency of Bayesian inference for multivariate max-stable distributions. Annals of Statistics, forthcoming.
  • Puy, A., Borgonovo, E., Lo Piano, S. et al. Irrigated areas drive irrigation water withdrawals. Nature Communications, 12, 4525.
  • Wade, S., Piccarreta, R., Cremaschi, A., Antoniano-Villalobos, I. (2021). Colombian women's life patterns: a multivariate density regression approach. Bayesian Analysis, forthcoming.
  • Zanella, G., and Roberts, G.O. (2021). Multilevel linear models, Gibbs samplers and multigrid decompositions. Bayesian Analysis (with discussion), 16, 1309-1391.

2020

  • Angelini, M. C., Lucibello, C., Parisi, G., Ricci-Tersenghi, F., and Rizzo, T. (2020). Loop expansion around the Bethe solution for the random magnetic field Ising ferromagnets at zero temperature. Proceedings of the National Academy of Sciences, 117, 2268-2274.
  • Antoniano-Villalobos, I., Borgonovo, E., and Lu, X. (2020). Nonparametric estimation of probabilistic sensitivity measures. Statistics and Computing, 30, 447-467.
  • Baldassi, C., Pittorino, F., and Zecchina, R. (2020). Shaping the learning landscape in neural networks around wide flat minima. Proceedings of the National Academy of Sciences, 117, 161-170.
  • Battigalli, P., Leonetti, P., and Maccheroni, F. (2020). Behavioral equivalence of extensive game structures. Games and Economic Behavior, 533-547.
  • Catalano, M., Lijoi, A., and Prünster, I. (2020). Approximation of Bayesian models for time-to-event data. Electronic Journal of Statistics, 14, 3366-3395.
  • Fortini, S., and Petrone, S. (2020). Quasi‐Bayes properties of a procedure for sequential learning in mixture models. Journal of the Royal Statistical Society: Series B, 82, 1087-1114.
  • Legramanti, S., Durante, D. and Dunson, D.B. (2020) Bayesian cumulative shrinkage for infinite factorizations. Biometrika, 107, 745–752.
  • Lijoi, A., Prünster, I., Rigon, T. (2020). The Pitman-Yor multinomial process for mixture modeling. Biometrika, 107, 891-906.
  • Lijoi, A., Prünster, I., and Rigon, T. (2020). Sampling hierarchies of discrete random structures. Statistics and Computing, 30, 1591-1607.
  • Lu, X., Rudi, A., Borgonovo, E., and Rosasco, L. (2020). Faster Kriging: Facing High-Dimensional Simulators. Operations Research, 68, 233-249.
  • Papaspiliopoulos, O., Roberts, G.O. and Zanella, G. (2020). Scalable inferences for crossed random effects models. Biometrika, 107, 25-40.
  • Zanella, G. (2020), Informed proposals for local MCMC in discrete spaces. Journal of the American Statistical Association, 852-865.

 

2019

  • Alfani, G., and Bonetti, M. (2019). A survival analysis of the last great European plagues: The case of Nonantola (Northern Italy) in 1630. Population Studies, 73, 101-118.
  • Arbel, J., De Blasi, P., Prünster, I. (2019). Stochastic approximations to the Pitman-Yor process. Bayesian Analysis, 15, 1201-1219.
  • Battigalli, P., Catonini, E., Lanzani, G., & Marinacci, M. (2019). Ambiguity attitudes and self-confirming equilibrium in sequential games. Games and Economic Behavior, 115, 1-29.
  • Battigalli, P., Francetich, A., Lanzani, G., and Marinacci, M. (2019). Learning and self-confirming long-run biases. Journal of Economic Theory, 183, 740-785.
  • Battigalli, P., Dufwenberg, M., and Smith, A. (2019). Frustration, aggression, and anger in leader-follower games. Games and Economic Behavior, 117, 15-39.
  • Billari, F. C., Hiekel, N., and Liefbroer, A. C. (2019). The social stratification of choice in the transition to adulthood. European Sociological Review, 35, 599-615.
  • Billari, F. C., Giuntella, O., and Stella, L. (2019). Does broadband Internet affect fertility?. Population Studies, 73, 297-316.
  • Camerlenghi, F., Lijoi, A., Orbanz, P. and Prünster, I. (2019). Distribution theory for hierarchical processes. The Annals of Statistics, 47, 67-92.
  • Camerlenghi, F., Dunson, D.B.., Lijoi, A., Prünster, I., Rodriguez, A. (2019). Latent nested nonparametric priors (with discussion). Bayesian Analysis, 14, 1303-1356.
  • Cerreia-Vioglio, S., Maccheroni, F., and Marinacci, M. (2019). Ambiguity aversion and wealth effects. Journal of Economic Theory, in press.
  • Durante, D. (2019). Conjugate Bayes for probit regression via unified skew-normal distributions. Biometrika, 106, 765–779.
  • Durante, D. and Rigon, T. (2019) Conditionally conjugate mean-field variational Bayes for logistic models. Statistical Science, 34, 472–485.
  • Falk, M., Padoan, S. A., and Wisheckel, F. (2019). Generalized Pareto copulas: A key to multivariate extremes. Journal of Multivariate Analysis, 174, 1-17.
  • Lee, A., Tiberi, S. and Zanella, G. (2019), Unbiased approximations of products of expectations. Biometrika, 106, 708–715.
  • Rigon, T., Durante, D. and Torelli, N. (2019). Bayesian semiparametric modelling of contraceptive behavior in India via sequential logistic regressions. Journal of the Royal Statistical Society: A, 182, 225–247.
  • Zanella, G., and G.O. Roberts (2019), Scalable Importance Tempering and Bayesian Variable Selection. Journal of the Royal Statistical Society: Series B, 81, 489-517.

 

2018

  • Aksoy, O., and Billari, F.C. (2018). Political Islam, marriage, and fertility: evidence from a natural experiment. American Journal of Sociology, 123, 1296-1340.
  • Antoniano‐Villalobos, I., Borgonovo, E., and Siriwardena, S. (2018). Which parameters are important? Differential importance under uncertainty. Risk Analysis, 38, 2459-2477.
  • Baldassi, C., and Zecchina, R. (2018). Efficiency of quantum vs. classical annealing in nonconvex learning problems. Proceedings of the National Academy of Sciences, 115, 1457-1462.
  • Billari, F. C., Hiekel, N., & Liefbroer, A. C. (2019). The social stratification of choice in the transition to adulthood. European Sociological Review, 35, 599-615. 
  • Borgonovo, E., Cappelli, V., Maccheroni, F., and Marinacci, M. (2018). Risk analysis and decision theory: A bridge. European Journal of Operational Research, 264, 280-293.
  • Borgonovo, E., Cillo, A., and Smith, C. L. (2018). On the relationship between safety and decision significance. Risk Analysis, 38, 1541-1558.
  • Camerlenghi, F., Lijoi, A. and Prünster, I. (2018). Bayesian nonparametric inference beyond the Gibbs-type framework. Scandinavian Journal of Statistics, 45, 1062-1091.
  • Canale, A., Durante, D. and Dunson, D.B. (2018). Convex mixture regression for quantitative risk assessment. Biometrics, 74, 1331–1340.
  • Durante, D. and Dunson, D.B. (2018). Bayesian inference and testing of group differences in brain networks. Bayesian Analysis, 13, 29–58.
  • Fortini, S., Petrone, S., and Sporysheva, P. (2018). On a notion of partially conditionally identically distributed sequences. Stochastic Processes and their Applications, 128, 819-846.
  • Guillou, A., Padoan, S. A., and Rizzelli, S. (2018). Inference for asymptotically independent samples of extremes. Journal of Multivariate Analysis, 167, 114-135.
  • Marinacci, M., and Severino, F. (2018). Weak time-derivatives and no-arbitrage pricing. Finance and Stochastics, 22, 1007-1036.

 

2017

  • Arbel, J., Prünster, I. (2017) A moment-matching Ferguson & Klass algortihm. Statistics and Computing, 27, 3-17.
  • Beranger, B., Padoan, S. A., and Sisson, S. A. (2017). Models for extremal dependence derived from skew‐symmetric families. Scandinavian Journal of Statistics, 44, 21-45
  • Borgonovo, E., and Cillo, A. (2017). Deciding with thresholds: Importance measures and value of information. Risk Analysis, 37(10), 1828-1848.
  • Canale, A., Lijoi, A., Nipoti, B. and Prünster, I. (2017). On the Pitman-Yor process with spike and slab base measure. Biometrika, 104, 681-697.
  • Canale, A., Prünster, I. (2017) Robustifying Bayesian nonparametric mixtures for count data. Biometrics, 73, 174-184
  • Cerreia-Vioglio, S., Maccheroni, F., and Marinacci, M. (2017). Stochastic dominance analysis without the independence axiom. Management Science, 63, 1097-1109.
  • Durante, D., Dunson, D. B. and Vogelstein, J. T. (2017). Nonparametric Bayes modeling of populations of networks. Journal of the American Statistical Association, 112, 1516–1530 (with discussion).
  • Durante, D., Mukherjee, N. and Steorts, R. C. (2017) Bayesian learning of dynamic multilayer networks. Journal of Machine Learning Research, 18, 1–29.
  • Durante, D., Paganin, S., Scarpa, B. and Dunson, D. B. (2017). Bayesian modelling of networks in complex business intelligence problems. Journal of the Royal Statistical Society: C, 66, 555–580.
  • Fortini, S., and Petrone, S. (2017). Predictive characterization of mixtures of Markov chains. Bernoulli, 23, 1538-1565.
  • Zanella, G., Kendall, W.S. and Bedard, M. (2017) A Dirichlet Form approach to MCMC optimal scaling. Stochastic Processes and their Applications, 127, 4053–4082.