Papers
Lambardi di San Miniato, M., Bellio, R., Grassetti L. and Vidoni, P. (2022). Separable spatio-temporal kriging for fast virtual sensing. Applied Stochastic Models in Business and Industry 1-24.
Battauz, M. and Vidoni, P. (2022). A likelihood-based boosting algorithm for factor analysis models with binary data. Computational Statistics and Data Analysis 168, 107412. Fonseca, G., Giummolè, F. and Vidoni, P. (2021). A note on simultaneous calibrated prediction intervals for time series. Statistical Methods & Applications 30, 317–330. Vidoni, P. (2021). Boosting multiplicative model combination. Scandinavian Journal of Statistics 48, 761-789. Grassetti, L., Bellio, R., Di Gaspero, L., Fonseca, G., and Vidoni, P. (2020). An extended regularized adjusted plus-minus analysis for lineup management in basketball using play-by-play data. IMA Journal of Management Mathematics 32, 385-409. Bozzo, E., Franceschet, M. and Vidoni, P. (2020). A parametric family of Massey-type methods: inference, prediction, and sensitivity. Journal of Quantitative Analysis in Sports 16, 255-269. Lancia, G. and Vidoni, P. (2020). Finding the largest triangle in a graph in expected quadratic time. European Journal of Operational Research 286, 458-467. Lagazio, C. and Vidoni, P. (2018). Calibrated prediction regions for Gaussian random fields. Environmetrics 29, e2495, doi 10.1002/env.2495. Vidoni, P. (2018). A note on predictive densities based on composite likelihood methods. Metron 76, 31-48. Franceschet, M., Bozzo, E. and Vidoni, P. (2017). The temporalized Massey’s method. Journal of Quantitative Analysis in Sports 13, 37-48. Vidoni, P. (2017). Improved multivariate prediction regions for Markov process models. Statistical Methods & Applications 26, 1-18; doi 10.1007/s10260-016-0362-y. Vidoni, P. (2015). Calibrated multivariate distributions for improved conditional prediction. Journal of Multivariate Analysis 142, 156-25. Vidoni, P. (2015). Estimating the Kullback-Liebler risk based on multifold cross-validation. Statistica Neerlandica 69, 510-540. Fonseca G., Giummolè F. and Vidoni P. (2014). Calibrating predictive distributions. Journal of Statistical Computation and Simulation 84, 373-383. Fonseca G., Giummolè F. and Vidoni P. (2012). A note about calibrated prediction regions and distributions. Journal of Statistical Planning and Inference 142, 2726-2734. Vidoni, P. (2010). Predictive densities and prediction limits based on predictive likelihoods. In Complex Data Modeling and Computationally Intensive Statistical Methods (Eds P. Mantovan, P. Secchi), Springer, 123-136. Giummolè, F. and Vidoni, P. (2010). Improved prediction limits for a general class of Gaussian models. Journal of Time Series Analysis 31, 483-493. Vidoni, P. (2009). A simple procedure for computing improved prediction intervals for autoregressive models. Journal of Time Series Analysis 30, 577-590. Vidoni, P. (2009). Improved prediction intervals and distribution functions. Scandinavian Journal of Statistics 36, 735-748. Varin, C. and Vidoni, P. (2009). Pairwise likelihood inference for general state space models. Econometric Reviews 28, 170-185. Vidoni, P. (2008). Improved predictive model selection. Journal of Statistical Planning and Inference 138, 3713-3721. Vidoni, P. (2006). Response prediction in mixed effects models. Journal of Statistical Planning and Inference 136, 3948-3966. Varin, C. and Vidoni, P. (2006). Pairwise likelihood inference for ordinal categorical time series. Computational Statistics and Data Analysis 51, 2365-2373. Varin, C. and Vidoni, P. (2005). A note on composite likelihood inference and model selection. Biometrika 92, 519-528. Vidoni, P. (2004). Improved prediction intervals for stochastic process models. Journal of Time Series Analysis 25, 137-154. Vidoni, P. (2004). Constructing non-linear Gaussian time series by means of a simplified state space representation. Studies in Nonlinear Dynamics and Econometrics 8 (2) art.9. Vidoni, P. (2003). Prediction and calibration in generalized linear models. Annals of the Institute of Statistical Mathematics 55, 169-185. Ferrante, M., Fonseca, G. and Vidoni, P. (2003). Geometric ergodicity, regularity of the invariant distribution and inference for a threshold bilinear Markov process. Statistica Sinica 13, 367-384. Vidoni, P. (2001). Improved prediction limits for continuous and discrete observations in generalized linear models. Biometrika 88, 881-887. Vidoni, P. (2001). Proper dispersion state space models for stochastic volatility. Scandinavian Journal of Statistics 28, 271-281. Vidoni, P. (2000). Model selection using the estimative and the approximate p* predictive densities. Annals of the Institute of Statistical Mathematics 52, 57-70. Vidoni, P. (1999). On predictive densities and prediction limits for scale and location models. Journal of the Italian Statistical Society 8/2-3, 205-211. Vidoni, P. (1999). Exponential family state space models based on a conjugate latent process. Journal of the Royal Statistical Society Ser. B 61, 213-221. Ferrante, M. and Vidoni, P. (1999). A Gaussian-generalized inverse Gaussian finite dimensional filter. Stochastic Processes and their Applications 84, 165-176. Ferrante, M. and Vidoni, P. (1998). Finite dimensional filters for nonlinear stochastic difference equations with multiplicative noise. Stochastic Processes and their Applications 77, 69-81. Vidoni, P. (1998). A note on modified estimative prediction limits and distributions. Biometrika 85, 949-953. Vidoni, P. (1995). A simple predictive density based on the p* formula. Biometrika 82, 855-863. ProceedingsLambardi di San Miniato, M., Bellio, R., Grassetti L. and Vidoni, P. (2022). Robust regression and adaptive filtering. Proceedings of the 36rd International Workshop on Statistical Modelling, University of Trieste (IT), July 2022, 211-216.
Battauz, M. and Vidoni, P. (2022). Boosting for variance components in mixed models. Proceedings of the 36rd International Workshop on Statistical Modelling, University of Trieste (IT), July 2022, 390-393. Lambardi di San Miniato, M., Bellio, R., Grassetti L. and Vidoni, P. (2022). Adaptive filters for time-varying correlation parameters. Book of short Papers SIS 2022. Editors: A. Balzanella, M. Bini, C. Cavicchia and R. Verde, 1400-1405, Pearson. Mameli, V. and Vidoni, P. (2022). Prediction intervals based on multiplicative model combinations. Book of short Papers SIS 2022. Editors: A. Balzanella, M. Bini, C. Cavicchia and R. Verde, 1871-1876, Pearson. Battauz, M. and Vidoni, P. (2021). Boosting Multidimensional IRT Models. CLADAG 2021: Book of Abstracts and Short Papers, Firenze, September 2021. Grassetti, L., Bellio, R., Fonseca, G., and Vidoni, P. (2019). Play-by-play data analysis for team managing in basketball. Proceedings of MathSport International 2020 Conference (Eds. Karlis, D., Ntzoufras, I., Drikos, S.), Propobos Publications, Athens, 129-139. Grassetti, L., Bellio, R., Fonseca, G., and Vidoni, P. (2019). Estimation of lineup efficiency effects in Basketball using play-by-play data. Smart Statistics for Smart Applications. Book of short Papers SIS 2019. Editors: G. Arbia, S. Peluso, A. Pini and G. Rivellini, 363-370, Pearson. Vidoni, P. (2018). Inference for multiplicative model combination using score matching. Proceedings of the 33rd International Workshop on Statistical Modelling Vol. 2, University of Bristol (UK), July 2018, 192-198. Fonseca, G., Giummolè, F. and Vidoni, P. (2018). Simultaneous calibrated prediction intervals for time series. Book of short Papers SIS 2018. Editors: A. Abbruzzo, E. Brentari, M. Chiodi and D. Piacentino, 1207-1212, Pearson. Vidoni, P. (2018). Improved bootstrap simultaneous prediction limits. Book of short Papers SIS 2018. Editors: A. Abbruzzo, E. Brentari, M. Chiodi and D. Piacentino, 892-897, Pearson Casella, C. and Vidoni, P. (2017). Formula 1 lap time modeling using generalized additive models. Proceedings of MathSport International 2017 Conference (Eds. De Francesco C., De Giovanni L., Ferrante M., Fonseca G., Lisi F., Pontarollo S.), Padova University Press, 78-96. Vidoni, P. (2013). Prediction based on composite likelihood. S.Co. 2013, Politecnico di MIlano. Electronic Proceedings S.Co. 2013. Bellio, R. and Vidoni, P. (2013). A note on improved random effects prediction in GLMMs. Proceedings of the 28th International Workshop on Statistical Modelling (Eds. Muggeo V.R., Capursi V., Boscaino G., Lovison G.), Palermo, 507-510. Fonseca, G., Giummolè, F. and Vidoni, P. (2011). Bootstrap calibrated predictive distributions for time series. S.Co. 2011, Padova. Electronic Proceedings S.Co. 2011. Fonseca, G., Giummolè, F. and Vidoni, P. (2011). Prediction in a multidimensional setting. Cladag 2011, Pavia. Electronic Proceedings Cladag 2011. Fonseca, G., Giummolè, F. and Vidoni, P. (2011). Predictive distributions for non-regular parametric models. Proceedings of the 26th International Workshop on Statistical Modelling (Eds. Conesa D., Forte A., Lopez-Quilez A., Munoz F.), Valencia, 220-223. Fonseca, G., Giummolè, F. and Vidoni, P. (2010). Improving estimative prediction regions. Proceedings of the 25th International Workshop on Statistical Modelling (Ed. A. W. Bowman), Glasgow, 201-204. Vidoni, P. (2004). Improved redictive model selection criteria for logistic regression. Proceedings of the 19th International Workshop on Statistical Modelling (Eds. A. Biggeri, E. Dreassi, C. Lagazio & M. Marchi), Firenze, July 2004, 499-503. Vidoni, P. (2001). Improved prediction intervals for stochastic process models. Atti del Convegno su Modelli Complessi e Metodi Computazionali Intensivi per la Stima e la Previsione, Bressanone, 24-26 September 2001, 481-485. Fonseca, G. and Vidoni, P. (2001). Improved prediction limits for a simple threshold bilinear model. Proceedings of the 16th International Workshop on Statistical Modelling (Eds. B. Klein & L. Korsholm), Odense, July 2001, 445-448. Vidoni, P. (1999). Prediction intervals for generalized linear models. Proceedings of the 14th International Workshop on Statistical Modelling (Eds. H. Friedl, A. Berghold & G. Kauermann), Graz, July 1999, 703-706. Working papersBellio, R. and Vidoni, P. (2016). Improved random effects prediction. Preprint.
Ronchetti, E. and Vidoni, P. (1998). A robust predictive density based on the saddlepoint approximation for M-estimators. Working paper 8.1998, Département d’Économétrie, Université de Genève (CH). |
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