Roberto Di Mari
Roberto Di Mari
UnniversitÓ degli Studi di Catania
Verified email at unict.it - Homepage
Title
Cited by
Cited by
Year
Bias-adjusted three-step latent Markov modeling with covariates
R Di Mari, DL Oberski, JK Vermunt
Structural Equation Modeling: A Multidisciplinary Journal 23 (5), 649-660, 2016
242016
A data driven equivariant approach to constrained Gaussian mixture modeling
R Rocci, SA Gattone, R Di Mari
Advances in Data Analysis and Classification 12 (2), 235-260, 2018
82018
Mostly harmless direct effects: A comparison of different latent Markov modeling approaches
R Di Mari, Z Bakk
Structural Equation Modeling: A Multidisciplinary Journal 25 (3), 467-483, 2018
82018
Clusterwise linear regression modeling with soft scale constraints
R Di Mari, R Rocci, SA Gattone
International Journal of Approximate Reasoning 91, 160-178, 2017
42017
Hierarchical Markov-switching models for multivariate integer-valued time-series
L Catania, R Di Mari
Journal of Econometrics, 2020
12020
Hierarchical hidden Markov models for multivariate integer-valued time-series
L Catania, R Di Mari
Technical report, SSRN, 2018
12018
A Random-covariate Approach for Distal Outcome Prediction with Latent Class Analysis
R Di Mari, Z Bakk, A Punzo
Structural Equation Modeling: A Multidisciplinary Journal 27 (3), 351-368, 2020
2020
Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models
R Di Mari, R Rocci, SA Gattone
Statistical Methods & Applications, 1-30, 2019
2019
Dynamic discrete mixtures for high frequency prices
L Catania, R Di Mari, P Santucci de Magistris
Available at SSRN 3349118, 2019
2019
Constrained maximum likelihood estimation of clusterwise linear regression models with unknown number of components
R Di Mari, R Rocci, SA Gattone
arXiv preprint arXiv:1804.05185, 2018
2018
Cluster-weighted latent class modeling
R Di Mari, A Punzo, Z Bakk
arXiv preprint arXiv:1801.01464, 2018
2018
Estimation of clusterwise linear regression models with a shrinkage-like approach
R Di Mari, R Rocci, SA Gattone
arXiv preprint arXiv:1611.03309, 2016
2016
Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation
R Di Mari, R Rocci, SA Gattone
International Conference on Soft Methods in Probability and Statistics, 181-186, 2016
2016
Covariate measurement error in generalized linear models for longitudinal data: a latent Markov approach
R Di Mari, A Punzo, A Maruotti
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Articles 1–14