Publications

Selected publications


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.
  • Ascolani, F., Lijoi, A. and Ruggiero, M. (2020). Predictive inference with Fleming-Viot driven dependent Dirichlet processes. Bayesian Analysis, in press.
  • 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.
  • 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, in press.
  • Battigalli, P., Leonetti, P., and Maccheroni, F. (2020). Behavioral equivalence of extensive game structures. Games and Economic Behavior, 533-547
  • Camerlenghi, F., Lijoi, A. and Prünster, I. (2020). Survival analysis via hierarchically dependent mixture hazards. The Annals of Statistics, in press.
  • Hashorva, E., Padoan, S. A., and Rizzelli, S. (2020). Multivariate extremes over a random number of observations. Scandinavian Journal of Statistics, in press.
  • Legramanti, S., Durante, D. and Dunson, D.B. (2020) Bayesian cumulative shrinkage for infinite factorizations. Biometrika, in press.
  • Lijoi, A., Prünster, I., Rigon, T. (2020). The Pitman-Yor multinomial process for mixture modeling. Biometrika, in press.
  • Lu, X., Rudi, A., Borgonovo, E., and Rosasco, L. (2020). Faster Kriging: Facing High-Dimensional Simulators. Operations Research, 68, 233-249.
  • Zanella, G. (2020), Informed proposals for local MCMC in discrete spaces. Journal of the American Statistical Association, in press.
  • Papaspiliopoulos, O., Roberts, G.O. and Zanella, G. (2020). Scalable inferences for crossed random effects models. Biometrika, 107, 25-40.


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.