(2023) Huang, Z. and Ferrari D., Fast Construction of Optimal Composite Likelihoods. Statistica Sinica (forthcoming).
(2022) Di Lascio, F. M. L., Falchetta, G., & Ferrari, D.. Change detection from high-resolution airborne laser scans using penalized composite likelihood screening. Spatial Statistics, 52, 100710.
(2022) Ferrari, D., Tonin, M. and Stillman, S., Assessing the impact of COVID19 mass testing in South Tyrol using a semiparametric growth model, Nature Scientific Reports 12:17952.
(2021) Huang, Z., Shulyarenko, O., and Ferrari, D.. Truncated pair-wise likelihood for the Brown-Resnick process with applications to maximum temperature data. Ex- tremes 24.3 (2021): 379-402.
(2021) Ferrari, D., Ravazzolo, F., and Vespignani, J. Forecasting energy commodity prices: A large global dataset sparse approach. Energy Economics, 98, 105268.
(2019) Li, Y., Luo, Y., Ferrari, D., Hu, X., & Qin, Y. (2019). Model confidence bounds for variable selection. Biometrics, 75(2), 392-403.
(2019) Lee, Amy H., ... Ferrari D., ... et al, Dynamic molecular changes during the first week of human life follow a robust developmental trajectory. Nature communications, 10(1), 1092.

(2019) Zheng, C., and Ferrari, D. , Model selection confidence sets by likelihood ratio testing. Statistica Sinica, Statistica Sinica, 29: 827–851.
(2018) Zheng, C., Zhang, M., Ferrari, D. and Baird, P., Ranking genetic factors related to age-related macular degeneration by variable selection confidence sets. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68: 727-749.
(2018) Qiao, P., Mølck, C., Ferrari, D., and Fred Hollande. A spatio-temporal model and inference tools for longitudinal count data on multicolor cell growth. The International Journal of Biostatistics 14.2 (2018): 20180008.

(2017) Huang, Z., Ferrari, D.  and Qian, G.. Parsimonious and powerful composite likelihood testing for group difference and genotype–phenotype association. Computational Statistics & Data Analysis, 110: 37-49.
(2016) Qian, G., Wu, Y.,  Ferrari, D., Qiao, P., and Hollande, F.  Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications. Computational intelligence and neuroscience.
(2016) Creed, S., Le C.,  Hassan, M., Pon, C, Albold, S.,  Chan, K.,  Berginsk, M.,  Huang, Z., Bear, J,  Lane, R.,  Halls, M., Ferrari, D.,Nowell, C., Sloan, E., beta2-adrenergic signaling  induces invadopodia for breast cancer cell invasion. Breast Cancer Research.
(2016) Le C. et al. Chronic Stress Remodels Lymph  Vasculature to Promote Tumor Cell Dissemination, Nature Communications, 7.
(2016) Ferrari, D. and Zheng, C., Reliable inference for complex models by discriminative composite likelihood estimation, Journal of Multivariate Analysis, 144: 68-80.
(2016) Giuzio, M., Ferrari, D. and Paterlini, S., Sparse and robust normal and t- portfolios by penalized Lq-likelihood minimization, European Journal of Operation research, 250(1), 251--261.
(2016) Ferrari, D.,  Qian, G., and Hunter, T., Parsimonious and Efficient Likelihood Composition by Gibbs Sampling, Journal of Computational and Graphical Statistics, 25:3 (2016): 935-953.
(2016) Bergamaschi, S., Ferrari, D., Guerra, F., Simonini, G., \and Velegrakis, Y.. Providing insight into data source topics. Journal on Data Semantics, 5(4), 211-228.
(2015) Ferrari, D. and Yang. Y., Confidence sets for model selection by F-testing, Statistica Sinica,   25, 1637--1658.
(2015) La Vecchia, D., Camponovo, L., and Ferrari D., Robust heart rate variability analysis  by generalized entropy minimization, Computational Statistics and Data  Analysis, 82: 137-151.
(2014) Kim-Fuchs, C.,  Le, C. P.,  Pimentel, M. A., Shackleford, D., Ferrari D.; Angst, E.,  Hollande, F., and
Sloan E., Chronic stress accelerates pancreatic cancer growth and invasion:  A critical role for beta-adrenergic signaling in the pancreatic microenvironment, Brain, Behavior, and Immunity.
(2013) Ferrari, D., Borrotti, M. and De March D., Response improvement in complex experiments by co-information composite likelihood optimization, Statistics and Computing, p. 1--13.
(2012) Ferrari, D. and La Vecchia, D. On robust estimation via pseudo-additive information, Biometrika, volume 99, issue 1, pages 238--244.
(2012) Bertoldi, C., Bellei, E., Pellacani, C., Ferrari, D., Lucchi, A., Cuoghi, A., Bergamini, S., Cortellini, P., Tomasi, A., Zeffe, D. and Monari, E., Non-bacterial protein expression in periodontal pockets by proteome analysis, Journal of
Clinical Periodontology.
2012) Lalla, M., Ferrari, D. and Frederic, P.  Unit nonresponse errors in income surveys: a case study. \emph{Quality \& Quantity}, Volume 46, Issue 6, pp 1769-1794.
(2011) Lalla, M., Frederic, P. and Ferrari, D., Students’ Evaluation of Teaching Effectiveness: Satisfaction and Related Factors (Attanasio M., Capursi V. - Statistical Methods for the Evaluation of University Systems - Springer Berlin Heidelberg (DEU)), pages  113--129.
(2011) Pistoresi B., Salsano F., Ferrari, D.  Political institutions and central bank independence revisited, Applied Economic Letters, Vol 18, pp. 679--682.
(2010)  Ferrari, D. and Yang, Y. Maximum lq-likelihood estimation. The Annals of Statistics, Vol.38, n.2, 753-78.
(2009)  Ferrari, D. and Paterlini, S. The Maximum Lq-Likelihood Method: an application to extreme quantile estimation in finance. Methodology and Computing in Applied Probability, Vol.11, n.1, 3-19.
(2009)  Ferrari, D., Read, D.  and van der Leeuw, S. An agent-based model of information flows in social dynamics,  in D. Lane, D. Pumain, S. van der Leeuw and G. West (eds.) Complexity Perspectives on Innovation and Social Change, Springer.
(2009) Villani, M., Bonacini S.,  Ferrari, D., Serra, R. and Lane, D. Exaptive processes: an agent based model, in D. Lane, D. Pumain, S. van der Leeuw and G. West (eds.) Complexity Perspectives on Innovation and Social
Change, Springer. Stochastic modeling.
(2007) Villani, M., Bonacini S., Ferrari, D., Serra, R. and Lane, D. An agent-based model of exaptative processes, European Management Review. Vol. 4, 141-151.