• Bertarelli, G., Schirripa Spagnolo, F., Salvati, N. & Pratesi, M. Small area estimation of agricultural data. Accettato per la pubblicazione (prevista: Febbraio 2021) in Spatial Econometric Methods in Agricultural Economics Using R, Taylor & Francis Inc. ISBN:978-1-498-76681-4
  • Bertarelli, G., Chambers, R. & Salvati, N. (2020). Outlier robust small domain estimation via bias correction and robust bootstrapping. Statistical Methods & Applications.
  • Salvati, N., Fabrizi, E., Ranalli, M.G. & Chambers, R. (2020) Small Area Estimation with Linked Data, forthcoming in Journal of Royal Statistical Society Series B, DOI: 10.1111/rssb.12401.
  • Schirripa-Spagnolo, F., Salvati, N. & D’Agostino, A. (2020). The use of sampling weights in M-quantile random-effects regression: an application to Programme for International Student Assessment mathematics scores, Journal of Royal Statistical Society Series C, 69, 991-1012.
  • Fiaschi, D., Giuliani, E., Nieri, F. & Salvati, N. (2020). How bad is your company? Measuring corporate wrongdoing beyond the magic of ESG metrics. Business Horizons, 63(3), 287-299.
  • Borgoni, R., Carcagni, A., Salvati, N. & Schmid, T. (2019) Analysing radon accumulation in the home by flexible M-quantile mixed effect regression. Stochastic Environmental Research and Risk Assessment, 33, 2, 375-394,
  • Frumento, P. & Salvati, N. (2020) Parametric Modelling of M-quantile Regression Coefficient Functions with Application to Small Area Estimation. Journal of the Royal Statistical Society, Series A, Volume 183, 229-250, DOI:10.1111/rssa.12495.
  • Fabrizi, E., Salvati, N. & Trivisano, C. (2020) Robust Bayesian small area estimation based in quantile regression. Computational Statistics and Data Analysis,
  • Marino, M.F., Ranalli, M.G., Salvati N. & Alfò, M. (2019) Semiparametric Empirical Best Prediction for Small Area Estimation of Unemployment  Indicators. Annals of Appied Statistics, 13, 2, 1166-1197,
  • Del Sarto, S., Marino, M.F., Ranalli, M.G. & Salvati, N. (2019) Using finite mixtures of M-quantile regression models to handle unobserved heterogeneity in assessing the effect of meteorology and traffic on air quality. Stochastic Environmental Research and Risk Assessment, 33, 7, pp. 1345-1359, 10.1007/s00477-019-01687-x.
  • Chandra, H., Chambers, R. & Salvati, N. (2019) Small Area Estimation of Survey Weighted Counts under Aggregated Level Spatial Models. Survey Methodology, 45, 1, 31-59.
  • Otto-Sobotka, F., Salvati, N., Ranalli, M.G. & Kneib, T. (2019) Adaptive semiparametric M-quantile regression. Econometrics and Statistics, 11, 116-129,
  • Mario Biggeri, David A. Clark, Andrea Ferrannini & Vincenzo Mauro (2019) Tracking the SDGs in an ‘integrated’ manner: A proposal for a new index to capture synergies and trade-offs between and within goals. World Development, 122, 628-647,
  • Marchetti S., Fabrizi E., Salvati N., Tzavidis N. Outlier robust area-level estimation using the M-quantile approach to small area, Conference on Current Trends in Survey Statistics 2019 (13-16 August 2019).
  • Marchetti Stefano, Small area poverty indicators adjusted using local price indexes, joint work with Gaia Bertarelli, Luigi Biggeri, Caterina Giusti, Monica Pratesi, Francesco Schirripa-Spagnolo, 62nd ISI World Statistics Congress 2019 (18-23 August, 2019).
  • Ranjbar Setareh, Salvati Nicola & Pacini Barbara, Robust Causal Inferences in Small Area Estimation, 62nd ISI World Statistics Congress 2019 (18-23 August, 2019).
  • F. Schirripa Spagnolo, R. Borgoni, A. Carcagni, A. Michelangeli, N. Salvati, Semiparametric M-quantile regression with measurement error correction, 13th International Conference on Computational and Financial Econometrics (University of London, UK, 14-16 December 2019).

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