Publications

2020

- Adda, J. Decker, C., Ottaviani, M. (2020). P-hacking 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., Ricci-Tersenghi, 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, 2268-2274.

- Antoniano-Villalobos, I., Borgonovo, E., 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, 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, 161-170.

- 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, 743-759.

- Carroni, E., Pin, P., Righi, S. (2020). Bring a friend! Privately or publicly? Management Science, 66, 2269-2290.

- Catalano, M., Lijoi, A., and Prünster, I. (2020). Approximation of Bayesian models for time-to-event data. Electronic Journal of Statistics, 14, 3366-3395.

- 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). Two-group Poisson-Dirichlet 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, 78-96.

- Falk, M., Padoan, S. A., Rizzelli, S. (2020).Strong convergence of multivariate maxima. Journal of Applied Probability, 57, 314-331.

- Favero, C. (2020). Why is COVID-19 mortality in Lombardy so high? Evidence from the simulation of a SEIHCR model. COVID Economics, 1, 47-62.

- 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.

- Giannetti, V., Rubera, G. (2020). Innovation for and from emerging countries: a closer look at the antecedents of trickle-down 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 semi-parametric integer-valued time series model. Entropy, 22, 69.

- Gueudré, T., Baldassi, C., Pagnani, A., Weigt, M. (2020). Predicting interacting protein pairs by coevolutionary paralog matching. Protein-protein 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). Utility-based model for characterizing the evolution of social networks. IEEE Transactions on Systems, Man and Cybernetics: Systems, 50, 1083-1094.

- Lijoi, A., Prünster, I., Rigon, T. (2020). The Pitman-Yor 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, 1591-1607.

- Lu, X., Rudi, A., Borgonovo E., Rosasco, L. (2020). Faster kriging: facing high-dimensional simulators Operations Research, 68, 233-249.

- Papaspiliopoulos, O., Roberts, G.O., Zanella, G. (2020). Scalable inference 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, 115, 852-865.

- 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 Pitman-Yor process. Bayesian Analysis, 15, 1201-1219.

- 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, 1303-1356.

- Camerlenghi, F., Lijoi, A., Orbanz, P., Prünster, I. (2019). Distribution theory for hierarchical process. The Annals of Statistics, 47, 67-92.

- Durante, D. (2019). Conjugate Bayes for probit regression via unified skew-normal distributions. Biometrika, 106, 765-779.

- Durante, D., Canale A., Rigon T. (2019). A nested expectation–maximization algorithm for latent class models with covariates. Statistics and Probability Letters, 146, 97-103

- Durante, D., Rigon, T. (2019). Conditionally conjugate mean-field variational Bayes for logistic models. Statistical Science, 34, 472-485.

- 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 W-NUT: The 5th Workshop on Noisy User-generated Text

- Fornaciari, T., Hovy, D. (2019). Geolocation with Attention-Based Multitask Learning Models. Proceedings of the 2019 EMNLP Workshop W-NUT: The 5th Workshop on Noisy User-generated Text

- Fornaciari, T., Hovy, D. (2019). Identifying Linguistic Areas for Geolocation. Proceedings of the 2019 EMNLP Workshop W-NUT: The 5th Workshop on Noisy User-generated Text

- Garimella, A., Banea, C., Hovy, D., Mihalcea, R. (2019). Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech 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 length-biased Weibull-Gamma frailty survival modeL. Statistics & Probability Letters, 153, 32-36.

- Giussani, A., Bonetti, M. (2019). Marshall–Olkin frailty survival models for bivariate right-censored failure time data. Journal of Applied Statistics, 46, 2945-2961.

- Henry, E., Ottaviani, M. (2019). Research and the approval process: the organization of persuasion. American Economic Review, 109, 911-955.

- 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 high-dimensional estimation. Proceedings of Machine Learning Research. Vol. 97: International Conference on Machine Learning, 9-15 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 W-NUT: The 5th Workshop on Noisy User-generated Text.

- Plischke, E., Borgonovo, E. (2019). Copula theory and probabilistic sensitivity analysis: is there a connection? European Journal of Operational Research, 277, 1046-1059.

- Purschke, C., Hovy, D. (2019). Lörres, Möppes, and the Swiss. (Re)Discovering regional patterns in anonymous social media data. Journal of Linguistic Geography, 7, 113-134.

- Rabitti, G., Borgonovo, E. (2019). A Shapley-Owen index for interaction quantification. SIAM/ASA Journal on Uncertainty Quantification, 7, 1060-1075.

- 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, 225-247.

2018

- Antoniano-Villalobos, I., Borgonovo, E., Siriwardena, S. N. (2018). Which parameters are important? Differential importance under uncertainty. Risk Analysis, 38, 2459-2477.

- Anzarut, M., Mena, R.H., Nava, C. and Prünster, I. (2018). Poisson driven stationary Markov models. Journal of Business and Economic Statistics, 36, 684-694.


- Baldassi, C., Zecchina, R. (2018). Efficiency of quantum vs. classical annealing in nonconvex learning problems. Proceedings of the National Academy of Sciences, 1457-1462.


- 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 low-precision 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, 239-244.


- Borgonovo, E., Buzzard, G., Wendell, R. (2018). A global tolerance approach to sensitivity analysis in linear programming. European Journal of Operational Research, 267, 321-337.


- Caglio, A. 2018 To disclose or not to disclose? An investigation of the antecedents and effects of open book accounting. European Accounting Review, 27, 263-287.


- Camerlenghi, F., Lijoi, A., Prünster, I. (2018). Bayesian nonparametric inference beyond the Gibbs-type framework. Scandinavian Journal of Statistics, 45, 1062-1091.


- Camerlenghi, F., Lijoi, A., Prünster, I. (2018). Density estimation via hierarchies of nonparametric priors. JSM proceedings, Section on Bayesian Statistical Science, ASA, 2569-2605.


- Canale, A., Durante, D., Dunson, D. B. (2018). Convex mixture regression for quantitative risk assessment. Biometrics, 74, 1331-1340.


- 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, 49-70.


- Ditillo, A., Caglio, A. (2018). Combining differentiated knowledge for innovation across organizations: the role of accounting and management controls. Accounting, innovation and inter-organisational relationships.


- Hovy, D., Fornaciari, T., (2018). Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 671-677.


- 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, 42-49.


- 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, 1-10.


- Li, Y., Liu, G., Pin, P. (2018). Network-based risk measurements for interbank systems. PLOS ONE 13, 1-18
- 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, 136-149 .

2017

- Aliee, H., Borgonovo, E., Glaß, M., Teich, J. (2017). On the Boolean extension of the Birnbaum importance to non-coherent systems. Reliability Engineering and System Safety, 160, 191-200.

- Arbel, J. and Prünster, I. (2017). A moment-matching Ferguson & Klass algortihm. Statistics and Computing, 27, 3-17.

- Arbel, J., Prünster, I. (2017). On the truncation error of a superposed gamma process. Springer Proceedings  in Mathematics and Statistics, 194, 151-159.

- Benavoli, A., Lijoi, A., Mira, A. (2017). Introduction to the special issue on Bayesian Nonparametrics International Journal of Approximate Reasoning, 83, 193-195.

- Borgonovo E. and Cillo A. (2017). Importance, Thresholds and Value of Information. Risk Analysis, 37, 1828-1848.

- Borgonovo E., and Iooss B. (2017). Moment Independent Importance Measures. Springer Handbook on Uncertainty Quantification, 1265-1287.

- Brummitt, C. D., Huremović, K., Pin, P., Bonds, M. H.,Vega-Redondo, 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 multiple-samples information Journal of Multivariate Analysis, 156, 18-28.

- Camerlenghi, F., Lijoi, A., Prünster, I. (2017). On some distributional properties of hierarchical processes JSM proceedings, Section on Bayesian Statistical Science, ASA, 853-860.

- Canale, A., Lijoi, A., Nipoti, B., Prünster, I. (2017). On the Pitman-Yor process with spike and slab base measure. Biometrika, 104, 681-697.

- Canale, A. and Prünster, I. (2017). Robustifying Bayesian nonparametric mixtures for count data. Biometrics, 73, 174-184

- Fortini, S., Petrone, S. (2017). Predictive Characterization of Mixtures of Markov Chains. Bernoulli, 23, 1538-1565.

- Gambardella, A., Raasch, C. and Von Hippel, E. (2017). The User Innovation Paradigm: Impacts on Markets and Welfare. Management Science, 63, 1450-1468.

- Kon Kam King, G., Arbel, J., Prünster, I. (2017). A Bayesian nonparametric approach to ecological risk assessment. Springer Proceedings in Mathematics and Statistics, 194, 11-19.

- 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, 1-17.

- Pin, P., Weidenholzer, E., Weidenholzer, S. (2017). Constrained mobility and the evolution of efficient outcomes Journal of Economic Dynamics and Control 82, 165-175.

- Zanella, G., Bedard, M., S Kendall, W. (2017). A Dirichlet form approach to MCMC optimal scaling Stochastic Processes and their Applications, 127, 4053-4082.

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, 308-327.

 

- 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, 153-162.

 

- Antoniano-Villalobos, 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 Host-based Learning Strategies. Organization Science, 27, 911-928.

 

- 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 skew-symmetric families. Scandinavian Journal of Statistics, 44, 21-45.

 

- Birhanu, A., Gambardella, A. and Valentini, G. (2016). Bribery and Investment: Firm-level Evidence from Africa and Latin America. Strategic Management Journal, 37, 1865-1877.

 

- 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 time-independent reliability importance measure. European Journal of Operational Research, 254, 427-442.

 

- Borgonovo, E., Plischke, E. (2016). Sensitivity analysis: A review of recent advances. European Journal of Operational Research, 248, 869-887.

 

- Buonaguidi, B. and Muliere, P. (2016). Bayesian sequential testing for Lévy processes with diffusion and jump components. Stochastics, 88, 1099-1113.

 

- Buonaguidi, B. and Muliere, P. (2016). Optimal sequential testing for an inverse Gaussian process. Sequential Analysis, 35, 69-83.

 

- Camerlenghi, F.,  Prünster I., Ruggiero, M. (2016). On time Gibbs-type random probability measures. JSM Proceedings, Section on Nonparamteric Statistics, ASA, 1969-1976.

 

- Carriero, A., Clark, T. and Marcellino, M. (2016). Common Drifting Volatility in Large Bayesian VARs. Journal of Business and Economic Statistics, 34, 375-390.

 

- Carriero, A., Kapetanios, G. and Marcellino, M. (2016). Structural analysis with Multivariate Autoregressive Index models. Journal of Econometrics, 192, 332-348.

- 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, 357-377.

 

- 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 ensemble-based sensitivity analysis of a passive containment cooling system of an AP1000 nuclear power plant. Reliability Engineering and System Safety, 151, 12-19.

- 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, 1-8.

 

- Favaro, S., Lijoi, A., Nava, C., Nipoti, B, Prünster, I. and Teh, Y.W. (2016). On the stick-breaking representation for homogeneous NRMIs. Bayesian Analysis, 11, 697-724.

 

- Favero, C., Gozluklu, A. and Yang, H. (2016). Demographics and the Behaviour of Interest Rates. IMF Economic Review, 64, 732-776.

- Foroni, C. and Marcellino, M. (2016). Mixed frequency structural VARs. Journal of the Royal Statistical Society, Series A, 179, 403-425. 

 

- Fosfuri A., Giarratana M.S. and Roca E. (2016). Social Business Hybrids: Demand Externalities, Competitive Advantage and Growth through Diversification. Organization Science, 25, 1275-1289.

 

- 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 Non-Standard Optimisation of Information Criteria and Variable Reduction Methods. Computational Statistics and Data Analysis, 100, 369-382.

- 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, 72-89.

 

- 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, 2179-2203.

 

- Manconi, A., Rizzo, A.E., and Spalt, O.G. (2016). Diversity investing. Working paper.

 

- Marcellino, M., Porqueddu, M. and Venditti, F. (2016). Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility. Journal of Business and Economic Statistics, 34, 118-127.

- 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 Antoniano-Villalobos, I. (2016). Bayesian Inference for the Extremal Dependence. Electronic Journal of Statistics, 10, 3310-3337.

 

- 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, 220-229.  

- 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, 1-9.