Our teams: EpiAgeing
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I undertook my PhD within a joint agreement between Universidade Federal de Minas Gerais (UFMG, Brazil) and Université Paris-Saclay (France). The objective of the PhD was to extend the use of quantile regression to longitudinal data. I developed two packages using R and C++ languages, which are available on the Comprehensive R Archive Network (CRAN).
I joined the EpiAgeing-CRESS team in 2023 to work on methodological aspects of accelerometer data on physical activity and sleep for their associations with dementia. These data can be analysed as time series and my current research project is to apply time series and machine learning methods in stochastic biological processes. |
Research interests
- Longitudinal data
- R and C++ languages
- Time series
- Alzheimer’s disease and related dementias
- Accelerometer
- Machine learning
- Quantile regression
Key publications
- Danilevicz IM, Ehlers RS. Bayesian influence diagnostics using normalized functional Bregman divergence. Communications in Statistics - Theory and Methods, 2022; 51(6):1637-1652.
View publication - Patrocinio PF, Reisen VA, Bondon P, Monte EZ, Danilevicz IM. Computational Economics, 2023.
View publication - Danilevicz IM, Bondon P, Reisen VA, Serpa FS. A longitudinal study of the influence of air pollutants on children. A robust multivariate approach. Journal Applied Statistics, 2023 (accepted).
View publication
