Massimo Cannas

发表

Prediction of late/early arrivals in container terminals – A qualitative approach

Claudia Pani, Thierry Vanelslander, Gianfranco Fancello, 2015, European Journal of Transport and Infrastructure Research.

An R package for propensity score matching with clustered data

Massimo Cannas, Bruno Arpino, Claudio Conversano, 2016 .

Random effects clustering in multilevel modeling: choosing a proper partition

Massimo Cannas, Claudio Conversano, Francesco Mola, 2018, Adv. Data Anal. Classif..

Machine learning for propensity score matching and weighting : comparing different estimation techniques and assessing ifferent balance diagnostics

Massimo Cannas, Bruno Arpino, B. Arpino, 2018 .

Comparing different approaches for propensity score matching with clustered data: a simulation study

Massimo Cannas, Bruno Arpino, B. Arpino, 2015 .

Matching with Clustered Data: the CMatching Package in R

Massimo Cannas, Bruno Arpino, B. Arpino, 2019, R J..

Propensity score matching with clustered data. An application to the estimation of the impact of caesarean section on the Apgar score

Massimo Cannas, Bruno Arpino, B. Arpino, 2016, Statistics in medicine.

Variation in caesarean delivery rates across hospitals: a Bayesian semi-parametric approach

Massimo Cannas, Claudio Conversano, Francesco Mola, 2017 .

Vessel arrival uncertainty in container terminals-different approaches based on machine learning methods

Claudia Pani, Thierry Vanelslander, Massimo Cannas, 2013 .

Machine learning for the estimation of the propensity score: a simulation study

Massimo Cannas, Bruno Arpino, 2016 .

Machine learning techniques for Propensity score matching with clustered data. A simulation study.

Massimo Cannas, Bruno Arpino, Francesco C. Billari, 2012 .

Delay prediction in container terminals: A comparison of machine learning methods

Claudia Pani, Massimo Cannas, Gianfranco Fancello, 2013 .