Luca Merlo
Associate Professor of Statistics at the Link Campus University
Department of Human Sciences
Link Campus University
Rome (RM), Italy
I obtained my PhD in Methodological Statistics at the Sapienza University of Rome (Italy) with a thesis on quantile regression models for the analysis of multivariate data. I actively collaborate with national and international institutions, including the Harvard T.H. Chan School of Public Health (US), the University of Southampton (United Kingdom) and the University of Pisa (Italy). From 2022 to 2024 I was a researcher in statistics at the European University of Rome (Italy). As of November, I am Associate Professor of Statistics at the Link Campus University in Rome (Italy). My research interests lie in quantile regression, latent variable models, finite mixture models with applications to longitudinal, time series and correlated data.
News
| Aug 22, 2025 | I will be chairing the session “Quantile methods and applications “ at the EcoSta 2025 conference hosted by the by the Waseda University, Tokyo. |
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| Jun 17, 2025 | I will be chairing the session “Innovative applications of latent Markov and semi-Markov models” at the 2025 SIS conference hosted by the University of Genova. |
| Jan 17, 2025 | Our paper with Marco Geraci and Lea Petrella, “Mid-quantile mixed graphical models with an application to mass public shootings in the US” has been published on the Journal of the Royal Statistical Society Series A: Statistics in Society. Check it out here. |
Selected Publications
2025
- Mid-quantile mixed graphical models with an application to mass public shootings in the U.S.Journal of the Royal Statistical Society Series A: Statistics in Society, 2025
2024
- Expectile hidden Markov regression models for analyzing cryptocurrency returnsStatistics and Computing, 2024
- Inter-order relations between equivalence for Lp-quantiles of the Student’s t distributionInsurance: Mathematics and Economics, 2024
- Childhood PM2.5 exposure and upward mobility in the United StatesProceedings of the National Academy of Sciences, 2024
2023
- Unified unconditional regression for multivariate quantiles, M-quantiles and expectilesJournal of the American Statistical Association, 2023
2022
- Quantile mixed hidden Markov models for multivariate longitudinal data: An application to children’s Strengths and Difficulties Questionnaire scoresJournal of the Royal Statistical Society Series C: Applied Statistics, 2022
- Marginal M-quantile regression for multivariate dependent dataComputational Statistics & Data Analysis, 2022
- Quantile hidden semi-Markov models for multivariate time seriesStatistics and Computing, 2022
2021
- Forecasting VaR and ES using a joint quantile regression and its implications in portfolio allocationJournal of Banking & Finance, 2021