| Long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis G Konstantinoudis, T Padellini, J Bennett, B Davies, M Ezzati, ... Environment international 146, 106316, 2021 | 165 | 2021 |
| Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework G Nicholson, B Lehmann, T Padellini, KB Pouwels, R Jersakova, J Lomax, ... Nature microbiology 7 (1), 97-107, 2022 | 34* | 2022 |
| Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: A population-based ecological study T Padellini, R Jersakova, PJ Diggle, C Holmes, RE King, BCL Lehmann, ... The Lancet Regional Health–Europe 15, 2022 | 20 | 2022 |
| Interoperability of statistical models in pandemic preparedness: Principles and reality G Nicholson, M Blangiardo, M Briers, PJ Diggle, TE Fjelde, H Ge, ... Statistical Science 37 (2), 183-206, 2022 | 17 | 2022 |
| Model-aware quantile regression for discrete data T Padellini, H Rue arXiv preprint arXiv:1804.03714, 2018 | 13* | 2018 |
| Persistence Flamelets: Topological Invariants for Scale Spaces T Padellini, P Brutti Journal of Computational and Graphical Statistics, 1-27, 2022 | 4* | 2022 |
| A Bayesian hierarchical small area population model accounting for data source specific methodologies from American Community Survey, Population Estimates Program, and … EN Peterson, RC Nethery, T Padellini, JT Chen, BA Coull, FB Piel, ... The Annals of Applied Statistics 18 (2), 1565-1595, 2024 | 3 | 2024 |
| A mathematical programming approach to sparse canonical correlation analysis L Amorosi, T Padellini, J Puerto, C Valverde Expert Systems with Applications, 121293, 2023 | 3 | 2023 |
| Supervised learning with indefinite topological Kernels T Padellini, P Brutti Statistics 55 (4), 765-786, 2021 | 3 | 2021 |
| Response to “Re: Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis” G Konstantinoudis, T Padellini, J Bennett, B Davies, M Ezzati, ... Environment International 150, 106427, 2021 | 1 | 2021 |
| Hierarchical Graphical Model for Learning Functional Network Determinants E Aliverti, L Forastiere, T Padellini, S Paganin, E Wit Studies in Neural Data Science: StartUp Research 2017, Siena, Italy, June 25 …, 2018 | | 2018 |
Marta BlangiardoProfessor of Biostatistics, MRC Centre for Environment and Health, Imperial CollegeVerified email at imperial.ac.uk
