Roberto Di Mari

Ricercatore di Statistica [SECS-S/01]


A) Peer-Reviewed Articles

  1. Di Mari, R., Bakk, Z., and Punzo, A. (2019). A random-covariate approach for distal outcome prediction with latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal (forthcoming).
  2. Di Mari, R., Rocci, R., and Gattone, S.A. (2019). Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models. Statistical Methods and Applications (forthcoming).
  3. Di Mari, R., and Bakk, Z. (2018). Mostly harmless direct effects: a comparison of different latent markov
    modeling approaches. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 467-483.
  4. Rocci, R., Gattone, S. A., and Di Mari, R. (2018). A data driven equivariant approach to constrained
    Gaussian mixture modeling. Advances in Data Analysis and Classification, 12(2), 235-260.
  5. Di Mari, R., Rocci, R., and Gattone, G. A. (2017). Clusterwise linear regression modeling with soft scale
    constraints. International Journal of Approximate Reasoning, 91, 160-178.
  6. Di Mari, R., Oberski, D.L, and Vermunt, J.K. (2016). Bias-adjusted three-step latent Markov modeling
    with covariates. Structural Equation Modeling: A Multidisciplinary Journal, 23(5), 649-660.

B) Book Chapters

  1. Di Mari R., Rocci R., Gattone S.A. (2017). Finite Mixture of Linear Regression Models: An Adaptive
    Constrained Approach to Maximum Likelihood Estimation. In: "Ferraro M. et al. (Eds.), Soft Methods
    for Data Science. Advances in Intelligent Systems and Computing"    , vol. 456. Springer, ISBN 978-3-
    319-42971-7.

C) Short Papers in Conference Proceedings

  1. Di Mari R., Bakk, Z. (2017). Stepwise latent Markov modeling with covariates in the presence of direct
    effects. In: "Greselin F. et al. (Eds.), Book of Short Papers CLADAG 2017", ISBN 978-88-99459-71-0.
  2. Di Mari, R., Maruotti, A. and Punzo, A. (2018). Covariate measurement error in generalized linear
    models for longitudinal data: a latent Markov approach. In: "Abbruzzo A. et al. (Eds.), Book of Short
    Papers SIS 2018", Pearson, ISBN 978-88-919102-33-0.