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Massimo Ventrucci

Personal Details

First Name:Massimo
Middle Name:
Last Name:Ventrucci
Suffix:
RePEc Short-ID:pve518

Affiliation

Dipartimento di Scienze Statistiche "Paolo Fortunati"
Alma Mater Studiorum - Università di Bologna

Bologna, Italy
http://www.stat.unibo.it/
RePEc:edi:dsbolit (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. L. Altieri & D. Cocchi & M. Ventrucci, 2025. "Entropy‐Based Assessment of Biodiversity, With Application to Ants' Nests Data," Environmetrics, John Wiley & Sons, Ltd., vol. 36(1), January.
  2. Yawen Guan & Garritt L Page & Brian J Reich & Massimo Ventrucci & Shu Yang, 2023. "Spectral adjustment for spatial confounding," Biometrika, Biometrika Trust, vol. 110(3), pages 699-719.
  3. Massimo Ventrucci & Daniela Cocchi & Gemma Burgazzi & Alex Laini, 2020. "PC priors for residual correlation parameters in one-factor mixed models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 745-765, December.
  4. Francesca Bruno & Fedele Greco & Massimo Ventrucci, 2016. "Non-parametric regression on compositional covariates using Bayesian P-splines," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 75-88, March.
  5. Francesca Bruno & Fedele Greco & Massimo Ventrucci, 2016. "Non-parametric regression on compositional covariates using Bayesian P-splines," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 75-88, March.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Francesca Bruno & Fedele Greco & Massimo Ventrucci, 2016. "Non-parametric regression on compositional covariates using Bayesian P-splines," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 75-88, March.

    Cited by:

    1. António Rua & Junho Lee & Miguel de Carvalho & Julio Avila, 2024. "Bayesian smoothing for time-varying extremal dependence," Working Papers w202406, Banco de Portugal, Economics and Research Department.
    2. Huiwen Wang & Zhichao Wang & Shanshan Wang, 2021. "Sliced inverse regression method for multivariate compositional data modeling," Statistical Papers, Springer, vol. 62(1), pages 361-393, February.

  2. Francesca Bruno & Fedele Greco & Massimo Ventrucci, 2016. "Non-parametric regression on compositional covariates using Bayesian P-splines," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 75-88, March.

    Cited by:

    1. António Rua & Junho Lee & Miguel de Carvalho & Julio Avila, 2024. "Bayesian smoothing for time-varying extremal dependence," Working Papers w202406, Banco de Portugal, Economics and Research Department.
    2. Huiwen Wang & Zhichao Wang & Shanshan Wang, 2021. "Sliced inverse regression method for multivariate compositional data modeling," Statistical Papers, Springer, vol. 62(1), pages 361-393, February.

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