| Methods for analyzing large spatial data: A review and comparison MJ Heaton, A Datta, A Finley, R Furrer, R Guhaniyogi, F Gerber, ... arXiv preprint arXiv:1710.05013 22, 2017 | 500* | 2017 |
| Spatio-temporal statistics with R CK Wikle, A Zammit-Mangion, N Cressie Chapman and Hall/CRC, 2019 | 369 | 2019 |
| Point process modelling of the Afghan War Diary A Zammit-Mangion, M Dewar, V Kadirkamanathan, G Sanguinetti Proceedings of the National Academy of Sciences 109 (31), 12414-12419, 2012 | 175 | 2012 |
| Spatial and temporal Antarctic Ice Sheet mass trends, glacio‐isostatic adjustment, and surface processes from a joint inversion of satellite altimeter, gravity, and GPS data A Martín‐Español, A Zammit‐Mangion, PJ Clarke, T Flament, V Helm, ... Journal of Geophysical Research: Earth Surface 121 (2), 182-200, 2016 | 149 | 2016 |
| Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods AL Ganesan, M Rigby, A Zammit-Mangion, AJ Manning, RG Prinn, ... Atmospheric Chemistry and Physics 14 (8), 3855-3864, 2014 | 142 | 2014 |
| FRK: An R package for spatial and spatio-temporal prediction with large datasets A Zammit-Mangion, N Cressie arXiv preprint arXiv:1705.08105, 2017 | 101 | 2017 |
| National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the Global Stocktake B Byrne, DF Baker, S Basu, M Bertolacci, KW Bowman, D Carroll, ... Earth System Science Data Discussions 2022, 1-59, 2022 | 84 | 2022 |
| Deep compositional spatial models A Zammit-Mangion, TLJ Ng, Q Vu, M Filippone Journal of the American Statistical Association 117 (540), 1787-1808, 2022 | 75 | 2022 |
| An assessment of forward and inverse GIA solutions for Antarctica A Martín‐Español, MA King, A Zammit‐Mangion, SB Andrews, P Moore, ... Journal of Geophysical Research: Solid Earth 121 (9), 6947-6965, 2016 | 69 | 2016 |
| Multivariate spatial covariance models: a conditional approach N Cressie, A Zammit-Mangion Biometrika 103 (4), 915-935, 2016 | 56 | 2016 |
| Deep integro-difference equation models for spatio-temporal forecasting A Zammit-Mangion, CK Wikle Spatial Statistics 37, 100408, 2020 | 52 | 2020 |
| Statistical deep learning for spatial and spatiotemporal data CK Wikle, A Zammit-Mangion Annual Review of Statistics and Its Application 10 (1), 247-270, 2023 | 46 | 2023 |
| Multivariate spatio‐temporal modelling for assessing Antarctica's present‐day contribution to sea‐level rise A Zammit‐Mangion, J Rougier, N Schön, F Lindgren, J Bamber Environmetrics 26 (3), 159-177, 2015 | 46 | 2015 |
| Constraining the mass balance of East Antarctica A Martin‐Español, JL Bamber, A Zammit‐Mangion Geophysical Research Letters 44 (9), 4168-4175, 2017 | 43 | 2017 |
| Likelihood-free parameter estimation with neural Bayes estimators M Sainsbury-Dale, A Zammit-Mangion, R Huser The American Statistician 78 (1), 1-14, 2024 | 41* | 2024 |
| Basis-function models in spatial statistics N Cressie, M Sainsbury-Dale, A Zammit-Mangion Annual Review of Statistics and Its Application 9 (1), 373-400, 2022 | 41 | 2022 |
| On statistical approaches to generate Level 3 products from satellite remote sensing retrievals A Zammit-Mangion, N Cressie, C Shumack Remote Sensing 10 (1), 155, 2018 | 33 | 2018 |
| Resolving the Antarctic contribution to sea‐level rise: a hierarchical modelling framework A Zammit‐Mangion, J Rougier, J Bamber, N Schön Environmetrics 25 (4), 245-264, 2014 | 31 | 2014 |
| Variational estimation in spatiotemporal systems from continuous and point-process observations A Zammit-Mangion, G Sanguinetti, V Kadirkamanathan IEEE Transactions on Signal Processing 60 (7), 3449-3459, 2012 | 27 | 2012 |
| Online variational inference for state-space models with point-process observations AZ Mangion, K Yuan, V Kadirkamanathan, M Niranjan, G Sanguinetti Neural computation 23 (8), 1967-1999, 2011 | 25 | 2011 |
Noel CressieDistinguished Professor, University of WollongongEmail verificata su uow.edu.au