Luca Merlo
Researcher (RTDA) in Statistics at the European University of Rome
Department of Human Sciences
European University of Rome
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). As of September 2022, I am a researcher in statistics at the European University of 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
| Sep 18, 2024 | Our paper with Sophie-An Kingsbury Lee and Francesca Dominici, “Childhood PM2.5 exposure and upward mobility in the United States” has been published on the Proceedings of the National Academy of Sciences of the United States of America. Check it out here. |
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| Feb 7, 2024 | Our paper with Valeria Bignozzi and Lea Petrella, “Inter-order relations between equivalence for 𝐿𝑝-quantiles of the Student’s 𝑡 distribution” has been published on Insurance: Mathematics and Economics. Check it out here. |
| Jan 13, 2024 | Our paper with Beatrice Foroni and Lea Petrella, “Expectile hidden Markov regression models for analyzing cryptocurrency returns” has been published on Statistics and Computing. Check it out here. |
Selected Publications
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