Publications
2020
 Adda, J. Decker, C., Ottaviani, M. (2020). Phacking in clinical trials and how incentives shape the distribution of results across phases. Proceedings of the
National Academy of Sciences of the USA, forthcoming.
 Angelini, M. C., Lucibello C., Parisi, G., RicciTersenghi, F., 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 of the USA, 117, 22682274.
 AntonianoVillalobos, I., Borgonovo, E., Lu, X. (2020). Nonparametric estimation of probabilistic sensitivity measures. Statistics and Computing, 30, 447467.
 Ascolani, F., Lijoi, A. and Ruggiero, M. (2020). Predictive inference with FlemingViot driven dependent Dirichlet processes. Bayesian Analysis, forthcoming.
 Baldassi, C., Pittorino, F., Zecchina, R. (2020). Shaping the learning landscape in neural networks around wide flat minima. Proceedings of the National Academy of Sciences of the USA, 117, 161170.
 Beranger, B., Padoan, S. A., Sisson, S. A. (2020). Estimation and uncertainty quantification for extreme quantile regions. Extremes, forthcoming.

Betancourt, B., Zanella, G. and Steorts, R. (2020). Random Partition Models for Microclustering Tasks. Journal of the American Statistical Association, forthcoming.
 Camerlenghi, F., Lijoi, A., Prünster, I. (2020). Survival analysis via hierarchically dependent mixture hazards. The Annals of Statistics, forthcoming.
 Carletti, E., Marquez, R., Petriconi S. (2020). The redistributive effects of bank capital regulation. Journal of Financial Economics, 136, 743759.
 Carroni, E., Pin, P., Righi, S. (2020). Bring a friend! Privately or publicly? Management Science, 66, 22692290.

Catalano, M., Lijoi, A., and Prünster, I. (2020). Approximation of Bayesian models for timetoevent data. Electronic Journal of Statistics, 14, 33663395.
 De Blasi, P., Martinez, A.E., Mena, R.H., Prünster, I. (2020). On the inferential implications of decreasing weight structures in mixture models. Computational Statistics & Data Analysis, 147, 106940.
 Denti, F., Guindani, M., Leisen, F., Lijoi, A., Vannucci, M. and Wadsworth, D. (2020). Twogroup PoissonDirichlet mixtures for multiple testing. Biometrics, forthcoming.
 Falk, M., Khorrami Chokami, A., Padoan, S. A. (2020). Records for some stationary dependence sequences. Journal of Applied Probability, 57, 7896.
 Falk, M., Padoan, S. A., Rizzelli, S. (2020).Strong convergence of multivariate maxima. Journal of Applied Probability, 57, 314331.
 Favero, C. (2020). Why is COVID19 mortality in Lombardy so high? Evidence from the simulation of a SEIHCR model. COVID Economics, 1, 4762.

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, 10871114.
 Giannetti, V., Rubera, G. (2020). Innovation for and from emerging countries: a closer look at the antecedents of trickledown and reverse innovation. Journal of the Academy of the Marketing Science, forthcoming.
 Graziadei, H., Lijoi, A., Lopes, H.F., Marques F., P.C, Prünster, I. (2020). Prior sensitivity analysis in a semiparametric integervalued time series model. Entropy, 22, 69.
 Gueudré, T., Baldassi, C., Pagnani, A., Weigt, M. (2020). Predicting interacting protein pairs by coevolutionary paralog matching. Proteinprotein interaction networks, Springer.
 Hashorva, E., Padoan S., Rizzelli, S. (2020). Multivariate extremes over a random number of observations. Scandinavian Journal of Statistics, forthcoming.
 Hoffmann, F., Inderst, R., Ottaviani, M. (2020). Persuasion through selective disclosure: implications for marketing, campaigning, and privacy regulation. Management Science, forthcoming.
 Li, Y., Luo, P., Pin, P. (2020). Utilitybased model for characterizing the evolution of social networks. IEEE Transactions on Systems, Man and Cybernetics: Systems, 50, 10831094.
 Lijoi, A., Prünster, I., Rigon, T. (2020). The PitmanYor multinomial model for mixture modelling. Biometrika, forthcoming.

Lijoi, A., Prünster, I., and Rigon, T. (2020). Sampling hierarchies of discrete random structures. Statistics and Computing, 30, 15911607.
 Lu, X., Rudi, A., Borgonovo E., Rosasco, L. (2020). Faster kriging: facing highdimensional simulators Operations Research, 68, 233249.
 Papaspiliopoulos, O., Roberts, G.O., Zanella, G. (2020). Scalable inference for crossed random effects models. Biometrika, 107, 2540.
 Zanella, G. (2020). Informed proposals for local MCMC in discrete spaces. Journal of the American Statistical Association, 115, 852865.

Zanella, G., and Roberts, G.O. (2020). Multilevel linear models, Gibbs samplers and multigrid decompositions. Bayesian Analysis (with discussion), forthcoming.
2019
 Aliverti, E., Durante, D. (2019). Spatial modeling of brain connectivity data via latent distance models with nodes clustering. Statistical Analysis and Data Mining
 Arbel, J., De Blasi, P., Prünster, I. (2019). Stochastic approximations to the PitmanYor process. Bayesian Analysis, 15, 12011219.
 Baldassi, C., Malatesta, E. M., Zecchina, R. (2019). Properties of the geometry of solutions and capacity of multilayer neural networks with rectified linear unit activations. Physical Review Letters 123.
 Bonetti, M., Cirillo, P., Ogay, A. (2019). Computing the exact distributions of some functions of the ordered multinomial counts: maximum, minimum, range and sums of order statistics. Royal Society Open Science, 6.
 Camerlenghi, F., Dunson, D.B., Lijoi, A., Prünster, I., Rodriguez, A. (2019). Latent nested nonparametric priors (with discussion). Bayesian Analysis, 15, 13031356.
 Camerlenghi, F., Lijoi, A., Orbanz, P., Prünster, I. (2019). Distribution theory for hierarchical process. The Annals of Statistics, 47, 6792.
 Durante, D. (2019). Conjugate Bayes for probit regression via unified skewnormal distributions. Biometrika, 106, 765779.
 Durante, D., Canale A., Rigon T. (2019). A nested expectation–maximization algorithm for latent class models with covariates. Statistics and Probability Letters, 146, 97103
 Durante, D., Rigon, T. (2019). Conditionally conjugate meanfield variational Bayes for logistic models. Statistical Science, 34, 472485.
 Falk, M., Padoan, S. A., Wisheckel, F. (2019). Generalized Pareto copulas: a key to multivariate extremes. Journal of Multivariate Analysis, 174.
 Fornaciari, T., Hovy, D. (2019). Dense Node Representation for Geolocation. Proceedings of the 2019 EMNLP Workshop WNUT: The 5th Workshop on Noisy Usergenerated Text
 Fornaciari, T., Hovy, D. (2019). Geolocation with AttentionBased Multitask Learning Models. Proceedings of the 2019 EMNLP Workshop WNUT: The 5th Workshop on Noisy Usergenerated Text
 Fornaciari, T., Hovy, D. (2019). Identifying Linguistic Areas for Geolocation. Proceedings of the 2019 EMNLP Workshop WNUT: The 5th Workshop on Noisy Usergenerated Text
 Garimella, A., Banea, C., Hovy, D., Mihalcea, R. (2019). Women’s Syntactic Resilience and Men’s Grammatical Luck: GenderBias in PartofSpeech Tagging and Dependency Parsing. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
 Giussani, A., Bonetti, M. (2019). A note on the lengthbiased WeibullGamma frailty survival modeL. Statistics & Probability Letters, 153, 3236.
 Giussani, A., Bonetti, M. (2019). Marshall–Olkin frailty survival models for bivariate rightcensored failure time data. Journal of Applied Statistics, 46, 29452961.
 Henry, E., Ottaviani, M. (2019). Research and the approval process: the organization of persuasion. American Economic Review, 109, 911955.
 Lee, A., Tiberi, S., Zanella, G. (2019). Unbiased approximations of products of expectations. Biometrika, 106, 708–715.
 Lucibello C., Saglietti L., Lu, Y. (2019). Generalized approximate survey propagation for highdimensional estimation. Proceedings of Machine Learning Research. Vol. 97: International Conference on Machine Learning, 915 June 2019, Long Beach, California, USA.
 Nguyen, H., Hovy, D. (2019). Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers. Proceedings of the 2019 EMNLP Workshop WNUT: The 5th Workshop on Noisy Usergenerated Text.
 Plischke, E., Borgonovo, E. (2019). Copula theory and probabilistic sensitivity analysis: is there a connection? European Journal of Operational Research, 277, 10461059.
 Purschke, C., Hovy, D. (2019). Lörres, Möppes, and the Swiss. (Re)Discovering regional patterns in anonymous social media data. Journal of Linguistic Geography, 7, 113134.
 Rabitti, G., Borgonovo, E. (2019). A ShapleyOwen index for interaction quantification. SIAM/ASA Journal on Uncertainty Quantification, 7, 10601075.
 Rigon, T., Durante, D., Torelli, N. (2019). Bayesian semiparametric modelling of contraceptive behaviour in India via sequential logistic regressions. Journal of the Royal Statistical Society Series A, 182, 225247.
2018
 AntonianoVillalobos, I., Borgonovo, E., Siriwardena, S. N. (2018). Which parameters are important? Differential importance under uncertainty. Risk Analysis, 38, 24592477.
 Anzarut, M., Mena, R.H., Nava, C. and Prünster, I. (2018). Poisson driven stationary Markov models. Journal of Business and Economic Statistics, 36, 684694.
 Baldassi, C., Zecchina, R. (2018). Efficiency of quantum vs. classical annealing in nonconvex learning problems. Proceedings of the National Academy of Sciences, 14571462.
 Baldassi, C., Gerace, F., Saglietti, L., Zecchina, R. (2018). From inverse problems to learning: a statistical mechanics approach. Journal of Physics: Conference Series 955.
 Baldassi, C., Gerace, F., Kappen, H. J., Lucibello, C., Saglietti, L., Tartaglione, E., Zecchina, R. (2018). Role of synaptic stochasticity in training lowprecision neural networks Physical Review Letters, 120.
 Boncinelli, L., Pin, P. (2018). The stochastic stability of decentralized matching on a graph. Games and Economic Behavior, 108, 239244.
 Borgonovo, E., Buzzard, G., Wendell, R. (2018). A global tolerance approach to sensitivity analysis in linear programming. European Journal of Operational Research, 267, 321337.
 Caglio, A. 2018 To disclose or not to disclose? An investigation of the antecedents and effects of open book accounting. European Accounting Review, 27, 263287.
 Camerlenghi, F., Lijoi, A., Prünster, I. (2018). Bayesian nonparametric inference beyond the Gibbstype framework. Scandinavian Journal of Statistics, 45, 10621091.
 Camerlenghi, F., Lijoi, A., Prünster, I. (2018). Density estimation via hierarchies of nonparametric priors. JSM proceedings, Section on Bayesian Statistical Science, ASA, 25692605.
 Canale, A., Durante, D., Dunson, D. B. (2018). Convex mixture regression for quantitative risk assessment. Biometrics, 74, 13311340.
 Cillo, P., Griffith, D. A., Rubera, G. (2018). The new product portfolio innovativeness–stock returns relationship: the role of large individual investors’ culture. Journal Of Marketing, 82, 4970.
 Ditillo, A., Caglio, A. (2018). Combining differentiated knowledge for innovation across organizations: the role of accounting and management controls. Accounting, innovation and interorganisational relationships.
 Hovy, D., Fornaciari, T., (2018). Increasing InClass Similarity by Retrofitting Embeddings with Demographic Information. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 671677.
 Hovy, D. (2018). The Social and the Neural Network: How to Make Natural Language Processing about People again. Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, 4249.
 Kume, A., Leisen, F., Lijoi, A. (2018). Limiting behaviour of the stationary search cost distribution driven by a generalized gamma process. Electronic Communications In Probability, 23, 110.
 Li, Y., Liu, G., Pin, P. (2018). Networkbased risk measurements for interbank systems. PLOS ONE 13, 118
 Saglietti, L., Gerace, F., Ingrosso, A., Baldassi, C., Zecchina, R. (2018). From statistical inference to a differential learning rule for stochastic neural networks. Interface Focus, 22.
 Russo, M., Durante, D., Scarpa, B. (2018). Bayesian inference on group differences in multivariate categorical data. Computational Statistics & Data Analysis, 126, 136149 .
2017
 Aliee, H., Borgonovo, E., Glaß, M., Teich, J. (2017). On the Boolean extension of the Birnbaum importance to noncoherent systems. Reliability Engineering and System Safety, 160, 191200.
 Arbel, J. and Prünster, I. (2017). A momentmatching Ferguson & Klass algortihm. Statistics and Computing, 27, 317.
 Arbel, J., Prünster, I. (2017). On the truncation error of a superposed gamma process. Springer Proceedings in Mathematics and Statistics, 194, 151159.
 Benavoli, A., Lijoi, A., Mira, A. (2017). Introduction to the special issue on Bayesian Nonparametrics International Journal of Approximate Reasoning, 83, 193195.
 Borgonovo E. and Cillo A. (2017). Importance, Thresholds and Value of Information. Risk Analysis, 37, 18281848.
 Borgonovo E., and Iooss B. (2017). Moment Independent Importance Measures. Springer Handbook on Uncertainty Quantification, 12651287.
 Brummitt, C. D., Huremović, K., Pin, P., Bonds, M. H.,VegaRedondo, F. (2017). Contagious disruptions and complexity traps in economic development Nature Human Behavour, 1, 665–672.
 Camerlenghi, F., Lijoi, A., Prünster, I. (2017). Bayesian prediction with multiplesamples information Journal of Multivariate Analysis, 156, 1828.
 Camerlenghi, F., Lijoi, A., Prünster, I. (2017). On some distributional properties of hierarchical processes JSM proceedings, Section on Bayesian Statistical Science, ASA, 853860.
 Canale, A., Lijoi, A., Nipoti, B., Prünster, I. (2017). On the PitmanYor process with spike and slab base measure. Biometrika, 104, 681697.
 Canale, A. and Prünster, I. (2017). Robustifying Bayesian nonparametric mixtures for count data. Biometrics, 73, 174184
 Fortini, S., Petrone, S. (2017). Predictive Characterization of Mixtures of Markov Chains. Bernoulli, 23, 15381565.
 Gambardella, A., Raasch, C. and Von Hippel, E. (2017). The User Innovation Paradigm: Impacts on Markets and Welfare. Management Science, 63, 14501468.
 Kon Kam King, G., Arbel, J., Prünster, I. (2017). A Bayesian nonparametric approach to ecological risk assessment. Springer Proceedings in Mathematics and Statistics, 194, 1119.
 Marcon, G., Padoan, S. A., Naveau P., Muliere P. and J. Segers (2017). Multivariate Nonparametric Estimation of the Pickands Dependence Function using Bernstein Polynomials. Journal of Statistical Planning and Inference, 183, 117.
 Pin, P., Weidenholzer, E., Weidenholzer, S. (2017). Constrained mobility and the evolution of efficient outcomes Journal of Economic Dynamics and Control 82, 165175.
 Zanella, G., Bedard, M., S Kendall, W. (2017). A Dirichlet form approach to MCMC optimal scaling Stochastic Processes and their Applications, 127, 40534082.
2016
 Adda, J., Dustmann, C. and Stevens, C. (2016). The Career Costs of Children. Journal of Political Economy, 125, 293  337.
 Amore, M. and Garofalo, O. (2016). Executive gender, competitive pressures, and corporate performance. Journal of Economic Behavior and Organization, 131, 308327.
 Andersson, U., Dasi, A., Mudambi, R. and Pedersen, T. (2016). Technology, Innovation and Knowledge: The Importance of Ideas and International Connectivity. Journal of World Business, 51, 153162.
 AntonianoVillalobos, I. and Walker, S.G. (2016). A Nonparametric Model for Stationary Time Series. Journal of Time Series Analysis, 37, 126–142.
 Asmussen, C.G., Larsen, M.M. and Pedersen, T. (2016). Organizational Adaptation in Offshoring: The Relative Performance of Home and Hostbased Learning Strategies. Organization Science, 27, 911928.
 Berchicci, L., Dutt, N. and Mitchell, W. (2016). Knowledge Sources and Waste Reduction: Less is More. Working paper.
 Beranger, B., Padoan, S.A. and Sisson, S.A. (2016). Models for extremal dependence derived from skewsymmetric families. Scandinavian Journal of Statistics, 44, 2145.
 Birhanu, A., Gambardella, A. and Valentini, G. (2016). Bribery and Investment: Firmlevel Evidence from Africa and Latin America. Strategic Management Journal, 37, 18651877.
 Bisetti, E., Favero, C. Nocera, G. and Tebaldi, C. (2016). A Multivariate Model of Strategic Asset Allocation with Longevity Risk, No. 10595, CEPR Discussion Papers.
 Borgonovo, E., Aliee, H., Glaß, M. and Teich, J., (2016). A new timeindependent reliability importance measure. European Journal of Operational Research, 254, 427442.
 Borgonovo, E., Plischke, E. (2016). Sensitivity analysis: A review of recent advances. European Journal of Operational Research, 248, 869887.
 Buonaguidi, B. and Muliere, P. (2016). Bayesian sequential testing for Lévy processes with diffusion and jump components. Stochastics, 88, 10991113.
 Buonaguidi, B. and Muliere, P. (2016). Optimal sequential testing for an inverse Gaussian process. Sequential Analysis, 35, 6983.
 Camerlenghi, F., Prünster I., Ruggiero, M. (2016). On time Gibbstype random probability measures. JSM Proceedings, Section on Nonparamteric Statistics, ASA, 19691976.
 Carriero, A., Clark, T. and Marcellino, M. (2016). Common Drifting Volatility in Large Bayesian VARs. Journal of Business and Economic Statistics, 34, 375390.
 Carriero, A., Kapetanios, G. and Marcellino, M. (2016). Structural analysis with Multivariate Autoregressive Index models. Journal of Econometrics, 192, 332348.
 Cillo, P. and Rubera, G. (2016). Blessed from birth? Using Twitter data to predict startup success. Working Paper.
 Cucurachi, S., Borgonovo, E. and Heijungs, R. (2016). A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment. Risk Analysis, 36, 357377.
 Del Fava, E., Piccarreta, R., Gregson, S. and Melegaro, A. (2016). Transition to Parenthood and HIV Infection in Rural Zimbabwe. PLoS One 11, e0163730.
 Di Maio, F., Nicola, G., Borgonovo, E. and Zio, E. (2016). Invariant methods for an ensemblebased sensitivity analysis of a passive containment cooling system of an AP1000 nuclear power plant. Reliability Engineering and System Safety, 151, 1219.
 Di Tillio, A., Ottaviani, M., and Sorensen, P.N. (2016). Strategic Sample Selection. Working paper.
 Dustmann, C. and Görlach, J.S. (2016). Estimating Immigrant Earnings Profiles when Migrations Are Temporary. Labour Economics, 41, 18.
 Favaro, S., Lijoi, A., Nava, C., Nipoti, B, Prünster, I. and Teh, Y.W. (2016). On the stickbreaking representation for homogeneous NRMIs. Bayesian Analysis, 11, 697724.
 Favero, C., Gozluklu, A. and Yang, H. (2016). Demographics and the Behaviour of Interest Rates. IMF Economic Review, 64, 732776.
 Foroni, C. and Marcellino, M. (2016). Mixed frequency structural VARs. Journal of the Royal Statistical Society, Series A, 179, 403425.
 Fosfuri A., Giarratana M.S. and Roca E. (2016). Social Business Hybrids: Demand Externalities, Competitive Advantage and Growth through Diversification. Organization Science, 25, 12751289.
 Henry, E. and Ottaviani, M. (2016). Wald Deconstructed: The Organization of Persuasion. Working paper.
 Kapetanios, G., Marcellino, M. and Papailias, F. (2016). Variable Selection for Large Unbalanced Datasets Using NonStandard Optimisation of Information Criteria and Variable Reduction Methods. Computational Statistics and Data Analysis, 100, 369382.
 Kon Kam King, G., Arbel, J.,
Prünster I., (2016). Bayesian Nonparametric Density Estimation in Ecotoxicology. 48e Journées de la Statistique de la SdSF, 6pp.
 Kremena, S., Fosfuri, A. and De Castro, J. (2016). Learning by Hiring: The Effect of Scientists’ Inbound Mobility on Research Performance in Academia. Organization Science, 27, 7289.
 Lijoi, A., Muliere, P., Prünster, I. and Taddei, F. (2016). Innovation, growth and aggregate volatility from a Bayesian nonparametric perspective. Electronic Journal of Statistics, 10, 21792203.
 Manconi, A., Rizzo, A.E., and Spalt, O.G. (2016). Diversity investing. Working paper.
 Marcellino, M., Porqueddu, M. and Venditti, F. (2016). Shortterm GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility. Journal of Business and Economic Statistics, 34, 118127.
 Marcellino, M. and Sivec, V. (2016). Monetary, Fiscal and Oil Shocks: Evidence based on Mixed Frequency Structural FAVARs. Journal of Econometrics, 193, 335–348.
 Marcon, G., Padoan, S.A., and AntonianoVillalobos, I. (2016). Bayesian Inference for the Extremal Dependence. Electronic Journal of Statistics, 10, 33103337.
 Molteni, L. and Ponce de Leon, J. (2016). Forecasting With Twitter Data: An Application To USA TV Series Audience. Special Issue on Big Data, International Journal of Design & Nature and Ecodynamics, 11, 220229.
 Rubera, G., Cillo, P., and Balocco, F. (2016). How should CEOs talk? Targeting and segmenting financial analysts with topic modeling. Working Paper.
 Torrisi, S., Gambardella, A., Giuri, P., Harhoff, D., Hoisl, K. and Mariani, M. (2016). Using, blocking, and sleeping patents: empirical evidence from a large inventor survey. Research Policy, 45, 1374–1385.
 Xiao, X., van Hoek, A.J., Kenward, M.G. Melegaro, A. and Jit, M.C. (2016). Clustering of contacts relevant to the spread of infectious disease. Epidemics, 17, 19.