Bignozzi, V., Merlo, L., & Petrella, L. (2024). Inter-order relations between equivalence for lp-quantiles of the student’s t distribution. Insurance: Mathematics and Economics, 116, 44–50.
Foroni, B., Merlo, L., & Petrella, L. (2024). Expectile hidden markov regression models for analyzing cryptocurrency returns. Statistics and Computing, 34(2), 66.
Foroni, B., Merlo, L., & Petrella, L. (2024). Quantile and expectile copula-based hidden markov regression models for the analysis of the cryptocurrency market. Statistical Modelling, 19.
Candila, V., Gallo, G. M., & Petrella, L. (2023). Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall. Annals of Operations Research, 1–34.
Laporta, A. G., Levantesi, S., & Petrella, L. (2023). Neural networks for quantile claim amount estimation: A quantile regression approach. Annals of Actuarial Science, 1–21.
Merlo, L., Dominici, F., Petrella, L., Salvati, N., & Wu, X. (2023). Estimating causal quantile exposure response functions via matching. arXiv Preprint arXiv:2308.01628.
Merlo, L., Geraci, M., & Petrella, L. (2023). Quantile mixed graphical models with an application to mass public shootings in the united states. arXiv Preprint arXiv:2309.05084.
Merlo, L., Maruotti, A., & Petrella, L. (2023). Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach. Statistical Modelling, 22(6), 485–508.
Merlo, L., Petrella, L., Salvati, N., & Tzavidis, N. (2023). Unified unconditional regression for multivariate quantiles, m-quantiles and expectiles. Journal of the American Statistical Association, 1–26.
Ranalli, M. G., Salvati, N., Petrella, L., & Pantalone, F. (2023). M-quantile regression shrinkage and selection via the lasso and elastic net to assess the effect of meteorology and traffic on air quality. Biometrical Journal, 2100355.
Andreani, M., Candila, V., & Petrella, L. (2022). Quantile regression forest for value-at-risk forecasting via mixed-frequency data. 13–18.
Bignozzi, V., Merlo, L., & Petrella, L. (2022). Inter-order relations between moments of a student distribution, with an application to -quantiles. arXiv Preprint arXiv:2209.12855.
Foroni, B., Merlo, L., & Petrella, L. (2022). Analyzing the correlation structure of financial markets using a quantile graphical model. Book of the Short Papers, 852–857.
Foroni, B., Merlo, L., & Petrella, L. (2022). Graphical models for commodities: A quantile approach. Methods and Applications in Fluorescence, 253–259.
Merlo, L., Petrella, L., & Salvati, N. (2022). Modeling unconditional m-quantiles in a regression framework. 1692–1695.
Merlo, L., Petrella, L., & Tzavidis, N. (2022). Quantile mixed hidden markov models for multivariate longitudinal data: An application to children’s strengths and difficulties questionnaire scores. Journal of the Royal Statistical Society Series C: Applied Statistics, 71.
Scarci, M., Raveglia, F., Bortolotti, L., Benvenuti, M., Merlo, L., Petrella, L., &... (2022). COVID-19 after lung resection in northern italy. Seminars in Thoracic and Cardiovascular Surgery, 34(2), 726–732.
Andreani, M., Candila, V., Morelli, G., & Petrella, L. (2021). Multivariate analysis of energy commodities during the COVID-19 pandemic: Evidence from a mixed-frequency approach. Risks, 9(8), 144.
Foroni, B., Morelli, G., & Petrella, L. (2021). The network of commodity risk. Energy Systems, 1–47.
Maruotti, A., Petrella, L., & Sposito, L. (2021). Hidden semi-markov-switching quantile regression for time series. Computational Statistics & Data Analysis, 159, 107208.
Merlo, L., Petrella, L., & Raponi, V. (2021). Forecasting VaR and ES using a joint quantile regression and its implications in portfolio allocation. Journal of Banking & Finance, 133, 106248.
Merlo, L., Petrella, L., Salvati, N., & Tzavidis, N. (2021). Marginal m-quantile regression for multivariate dependent data. Computational Statistics & Data Analysis, 173, 107500.
Sciacchitano, S., Capalbo, C., Napoli, C., Negro, A., De Biase, L., Marcolongo, A., &... (2021). Nonthyroidal illness syndrome (NTIS) in severe COVID-19 patients: Role of T3 on the na/k pump gene expression and on hydroelectrolytic equilibrium. Journal of Translational Medicine, 19(1), 1–18.
Bignozzi, V., Macci, C., & Petrella, L. (2020). Large deviations for method-of-quantiles estimators of one-dimensional parameters. Communications in Statistics-Theory and Methods, 49(5), 1132–1157.
Candila, V., Gallo, G. M., & Petrella, L. (2020). Mixed-frequency quantile regression with realized volatility to forecast value-at-risk.
Candila, V., Gallo, G. M., & Petrella, L. (2020). Using mixed-frequency and realized measures in quantile regression.
Fabrizi, E., Salvati, N., & Trevisano, C. (2020). Small area estimation based on flexible quantile regression. Computational Statistics and Data Analysis.
Frumento, P., & Salvati, N. (2020). Parametric modelling of m-quantile regression coefficient functions with application to small area estimation. Journal of Royal Statistical Society Series A, 183, 229–250.
Marinozzi, F., Carleo, F., Novelli, S., Di Martino, M., Cardillo, G., Petrella, L., &... (2020). 3D reconstruction model of an extra-abdominal desmoid tumor: A case study. Frontiers in Bioengineering and Biotechnology, 8, 518.
Maruotti, A., Merlo, L., & Petrella, L. (2020). A two-part finite mixture quantile regression model for semi-continuous longitudinal data.
Merlo, L., Petrella, L., & Raponi, V. (2020). Sectoral decomposition of CO2 world emissions: A joint quantile regression approach. International Review of Environmental and Resource Economics, 14(2-3), 197–239.
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.
Borgoni, R., Carcagnı̀, 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, 375–394.
Chambers, R., Salvati, N., Fabrizi, E., & Silva, A. D. da. (2019). Domain estimation under informative linkage. Statistical Theory and Related Fields. https://doi.org/10.1080/24754269.2019.1653158
Chandra, H., Salvati, N., & Chambers, R. (2019). Small area estimation of survey weighted counts under aggregated level spatial models. Survey Methodology, 45, 31–59.
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, 1345–1359.
Marino, M. F., Ranalli, M. G., Salvati, N., & Alfò, M. (2019). Semi-parametric empirical best prediction for small area estimation of unemployment indicators. Annals of Applied Statistics, 13, 1166–1197.
Petrella, L., Laporta, A. G., & Merlo, L. (2019). Cross-country assessment of systemic risk in the european stock market: Evidence from a CoVaR analysis. Social Indicators Research, 146(1-2), 169–186.
Petrella, L., & Raponi, V. (2019). Joint estimation of conditional quantiles in multivariate linear regression models with an application to financial distress. Journal of Multivariate Analysis, 173, 70–84.
Sobotka, F. O., Kneib, T., Salvati, N., & Ranalli, M. G. (2019). Adaptive semiparametric m-quantile regression. Econometrics and Statistics, 11, 116–129.
Baldermann, C., Salvati, N., & Schmid, T. (2018). Robust small area estimation under spatial non-stationarity. International Statistical Review. https://doi.org/10.1111/insr.12245
Bianchi, A., Fabrizi, E., Salvati, N., & Tzavidis, N. (2018). Estimation and testing in m-quantile regression with applications to small area estimation. International Statistical Review. https://doi.org/10.1111/insr.12267
Bignozzi, V., Macci, C., & Petrella, L. (2018). Large deviations for risk measures in finite mixture models. Insurance: Mathematics and Economics, 80, 84–92.
Borgoni, R., Del Bianco, P., Salvati, N., Schmid, T., & Tzavidis, N. (2018). Modelling the distribution of health-related quality of life of advanced melanoma patients in a longitudinal multi-centre clinical trial using m-quantile random effects regression. Statistical Methods in Medical Research. https://doi.org/10.1177/0962280216636651
Chandra, H., Salvati, N., & Chambers, R. (2018). Small area prediction under nonparametric generalized linear mixed model. Journal of Computational Statistics and Data Analysis, 126, 19–38.
Costantino, F., Di Gravio, G., Patriarca, R., & Petrella, L. (2018). Spare parts management for irregular demand items. Omega, 81, 57–66.
Laporta, A. G., Merlo, L., & Petrella, L. (2018). Selection of value at risk models for energy commodities. Energy Economics, 74, 628–643.
Marchetti, S., Berkesewicz, M., Salvati, N., Szymkowiak, M., & Wawrowski, L. (2018). The use of a three-level m-quantile model to map poverty at local administrative unit 1 in poland. Journal of Royal Statistical Society Series A. https://doi.org/10.1111/rssa.12348
Merlo, L., Maruotti, A., Petrella, L., & Punzo, A. (2018). Quantile hidden semi-markov models for multivariate time series. Statistics and Computing, 32(4), 61.
Alfò, M., Salvati, N., & Ranalli, M. G. (2017). Finite mixtures of quantile and m-quantile regression models. Statistics & Computing, 27, 547–570.
Chandra, H., & Salvati, N. (2017). Small area estimation of proportions under a spatial dependent aggregated level spatial models. Communications In Statistics. Theory And Methods. https://doi.org/10.1080/03610926.2017.1317806
Chandra, H., Salvati, N., & Chambers, R. (2017). Small area prediction of counts under a non-stationary spatial model. Spatial Statistics, 20, 30–56.
Marchetti, S., Giusti, C., Salvati, N., & Pratesi, M. (2017). Small area estimation based on m-quantile models in presence of outliers in auxiliary variables. Statistical Methods & Applications. https://doi.org/10.1007/s10260-017-0380-4
Mollica, C., & Petrella, L. (2017). Bayesian binary quantile regression for the analysis of bachelor-to-master transition. Journal of Applied Statistics, 44(15), 2791–2812.
Schirripa-Spagnolo, F., D’Agostino, A., & Salvati, N. (2017). Measuring differences in economic standard of living between immigrant communities in italy. Quality And Quantity. https://doi.org/10.1007/s11135-017-0542-3
Schmid, T., Bruckschen, F., Salvati, N., & Zbiranski. (2017). Constructing socio-demographic indicators for NSI using mobile data. Journal of Royal Statistical Society Series A. https://doi.org/10.1111/rssa.12305
Chambers, R., Salvati, N., & Tzavidis, N. (2016). Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK. Journal of Royal Statistical Society Series A, 179, 453–479.
Tzavidis, N., Salvati, N., Schmid, T., Flouri, E., & Midouhas, E. (2016). Longitudinal analysis of the strengths and difficulties questionnaire scores of the millennium cohort study children in england using m-quantile random effects regression. Journal of Royal Statistical Society Series A, 179, 427–452.
Bianchi, A., & Salvati, N. (2015). Asymptotic properties and variance estimators of the m-quantile regression coefficients estimators. Communications in Statistics - Theory and Methods, 44, 2416.
Bianchi, A., & Salvati, N. (2014). Asymptotic properties and variance estimators of the m-quantile regression coefficients estimators. Communications in Statistics - Theory and Methods. https://doi.org/1080/03610926.2013.791375
Chambers, R., Dreassi, E., & Salvati, N. (2014). Disease mapping via negative binomial regression m-quantiles. Statistics in Medicine, 33, 4805–4824.
Dreassi, E., Ranalli, M. G., & Salvati, N. (2014). Semiparametric m-quantile regression for count data. Statistical Methods in Medical Research, 23, 591–610.
Giusti, C., Tzavidis, N., Pratesi, M., & Salvati, N. (2014). Resistance to outliers of m-quantile and robust random effects small area models. Communications in Statistics - Simulation and Computation, 43, 549–568.
Chandra, H., Salvati, N., Chambers, R., & Tzavidis, N. (2012). Small area estimation under spatial nonstationarity. Journal of Computational Statistics and Data Analysis, 56, 2875–2888.
Fabrizi, E., Salvati, N., & Pratesi, M. (2012). Constrained small area estimators based on m-quantile methods. Journal of Official Statistics, 28, 1–15.
Giusti, C., Marchetti, S., Pratesi, M., & Salvati, N. (2012). Robust small area estimation and oversampling in the estimation of poverty indicators. Survey Research Methods, 3, 155–163.
Salvati, N., Tzavidis, Pratesi, M., & Chambers, R. (2012). Small area estimation via m-quantile geographically weighted regression. TEST, 21, 1–28. https://doi.org/10.1007/s11749-010-0231-1
Tzavidis, N., Salvati, N., Chambers, R., & Chandra, H. (2012). Small area estimation in practice: An application to agricultural business survey data. Journal of the Indian Society of Agricultural Statistics, 66, 213–238.


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