Roberto Di Mari

Ricercatore di Statistica [SECS-S/01]

A) Peer-Reviewed Articles

  1. 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.
  2. 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.
  3. 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.
  4. 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-

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.