By Associate Professor Andrew Zammit Mangion

Books

Wikle CK; Zammit-Mangion A; Cressie N, 2019, SPATIO-TEMPORAL STATISTICS WITH R, http://dx.doi.org/10.1201/9781351769723

Zammit-Mangion A; Dewar M; Kadirkamanathan V; Flesken A; Sanguinetti G, 2013, Modeling conflict dynamics with spatio-temporal data, http://dx.doi.org/10.1007/978-3-319-01038-0

Book Chapters

Gopalan G; Zammit-Mangion A; McCormack F, 2023, 'A Review of Bayesian Modelling in Glaciology', in Statistical Modeling Using Bayesian Latent Gaussian Models with Applications in Geophysics and Environmental Sciences, pp. 81 - 107, http://dx.doi.org/10.1007/978-3-031-39791-2_2

Journal articles

Vercelloni J; Logan M; Zammit-Mangion A; Sainsbury-Dale M; Schaffelke B; Mengersen K; González-Rivero M, 2026, 'Predicting Coral Cover Trends From Local to Broad Spatial Scales', Global Change Biology, 32, http://dx.doi.org/10.1111/gcb.70998

de Kreij R; Zammit-Mangion A; Rayson M; Jones N; Zulberti A, 2026, 'Statistical Inversion of Sea Surface Temperature to Predict Submesoscale Near-Surface Ocean Currents', Journal of Advances in Modeling Earth Systems, 18, http://dx.doi.org/10.1029/2025MS005424

Stephens BB; Jin Y; Sweeney C; McKain K; Gaubert B; Baker DF; Basu S; Bertolacci M; Chevallier F; Commane R; Crowell S; Deng F; Johnson MS; Keeling RF; Liu J; Liu Z; Maity S; Morgan EJ; Patra P; Philip S; Wofsy SC; Zammit-Mangion A, 2026, 'Improved latitudinal carbon budgets from global airborne surveys', Proceedings of the National Academy of Sciences of the United States of America, 123, http://dx.doi.org/10.1073/pnas.2523984123

Vu BA; Gunawan D; Zammit-Mangion A, 2026, 'Recursive variational Gaussian approximation with the Whittle likelihood for linear non-Gaussian state space models', Computational Statistics and Data Analysis, 218, http://dx.doi.org/10.1016/j.csda.2025.108324

Valderrama-Giraldo J; Liu Q; Bertolacci M; Bransby F; Watson P; Zammit-Mangion A, 2026, 'Comparison of Approaches to Data-Driven Site Characterization in an Offshore Carbonate Soil Environment in North Western Australia', ASCE ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering, 12, http://dx.doi.org/10.1061/AJRUA6.RUENG-1679

Jacobson J; Bertolacci M; Zammit-Mangion A; Schuh A; Cressie N, 2025, 'WOMBAT v2.S: A Bayesian Inversion Framework for Attributing Global CO2 Flux Components From Multiprocess Data', Environmetrics, 36, http://dx.doi.org/10.1002/env.70052

Huser R; Zammit-Mangion A, 2025, 'Raphaël Huser and Andrew Zammit-Mangion's contribution to the Discussion of the 'Discussion Meeting on the Analysis of citizen science data', Journal of the Royal Statistical Society Series A Statistics in Society, 188, pp. 714 - 716, http://dx.doi.org/10.1093/jrsssa/qnaf010

Zheng X; Cressie N; Clarke DA; McGeoch MA; Zammit-Mangion A, 2025, 'Spatial-statistical downscaling with uncertainty quantification in biodiversity modelling', Methods in Ecology and Evolution, 16, pp. 837 - 853, http://dx.doi.org/10.1111/2041-210X.14505

Zammit-Mangion A; Sainsbury-Dale M; Huser R, 2025, 'Neural Methods for Amortized Inference', Annual Review of Statistics and Its Application, 12, pp. 311 - 335, http://dx.doi.org/10.1146/annurev-statistics-112723-034123

Bertolacci M; Zammit-Mangion A; Valderrama Giraldo J; O’Neill M; Bransby F; Watson P, 2025, 'GeoWarp: Warped Spatial Processes for Inferring Subsea Sediment Properties', Journal of the American Statistical Association, 120, pp. 710 - 722, http://dx.doi.org/10.1080/01621459.2024.2445874

Sainsbury-Dale M; Zammit-Mangion A; Richards J; Huser R, 2025, 'Neural Bayes Estimators for Irregular Spatial Data Using Graph Neural Networks', Journal of Computational and Graphical Statistics, 34, pp. 1153 - 1168, http://dx.doi.org/10.1080/10618600.2024.2433671

Huser R; Zammit-Mangion A, 2025, 'Raphaël Huser and Andrew Zammit-Mangion’s contribution to the Discussion of the ‘Discussion Meeting on the Analysis of citizen science data’', Journal of the Royal Statistical Society Series A Statistics in Society, 188, pp. 714 - 716, http://dx.doi.org/10.1093/jrsssa/qnaf012

Vu Q; Moores MT; Zammit-Mangion A, 2025, 'Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants', Bayesian Analysis, 20, pp. 435 - 459, http://dx.doi.org/10.1214/23-BA1400

Vu BA; Gunawan D; Zammit-Mangion A, 2024, 'Correction to: R-VGAL: a sequential variational Bayes algorithm for generalised linear mixed models (Statistics and Computing, (2024), 34, 3, (110), 10.1007/s11222-024-10422-8)', Statistics and Computing, 34, http://dx.doi.org/10.1007/s11222-024-10469-7

Vu BA; Gunawan D; Zammit-Mangion A, 2024, 'R-VGAL: a sequential variational Bayes algorithm for generalised linear mixed models', Statistics and Computing, 34, http://dx.doi.org/10.1007/s11222-024-10422-8

Zammit-Mangion A; Kaminski MD; Tran BH; Filippone M; Cressie N, 2024, 'Spatial Bayesian neural networks', Spatial Statistics, 60, http://dx.doi.org/10.1016/j.spasta.2024.100825

Mackintosh A; McCormack F; Jones R; Purich A; Beckmann J; Tielidze L; Saunderson D; Macha J; Rand C; Bird L; Strugnell J; Lau S; Smith J; McNeil M; Whitmore R; McLennan S; Fulop R; Zammit Mangion A; Henley B; McGregor H, 2024, 'Securing Antarctica’s Environmental Future: the East Antarctic Ice Sheet ', , http://dx.doi.org/10.5194/egusphere-egu24-12985

Bertolacci M; Zammit-Mangion A; Schuh A; Bukosa B; Fisher JA; Cao Y; Kaushik A; Cressie N, 2024, 'INFERRING CHANGES TO THE GLOBAL CARBON CYCLE WITH WOMBAT V2.0, A HIERARCHICAL FLUX-INVERSION FRAMEWORK', Annals of Applied Statistics, 18, pp. 303 - 327, http://dx.doi.org/10.1214/23-AOAS1790

Ng TLJ; Zammit-Mangion A, 2024, 'Mixture modeling with normalizing flows for spherical density estimation', Advances in Data Analysis and Classification, 18, pp. 103 - 120, http://dx.doi.org/10.1007/s11634-023-00561-7

Sainsbury-Dale M; Zammit-Mangion A; Huser R, 2024, 'Likelihood-Free Parameter Estimation with Neural Bayes Estimators', American Statistician, 78, pp. 1 - 14, http://dx.doi.org/10.1080/00031305.2023.2249522

Sainsbury-Dale M; Zammit- Mangion A; Cressie N, 2024, 'Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK', Journal of Statistical Software, 108, pp. 1 - 39, http://dx.doi.org/10.18637/jss.v108.i10

Richards J; Sainsbury-Dale M; Zammit-Mangion A; Huser R, 2024, 'Neural Bayes estimators for censored inference with peaks-over-threshold models', Journal of Machine Learning Research, 25

Gaubert B; Stephens BB; Baker DF; Basu S; Bertolacci M; Bowman KW; Buchholz R; Chatterjee A; Chevallier F; Commane R; Cressie N; Deng F; Jacobs N; Johnson MS; Maksyutov SS; McKain K; Liu J; Liu Z; Morgan E; O’Dell C; Philip S; Ray E; Schimel D; Schuh A; Taylor TE; Weir B; van Wees D; Wofsy SC; Zammit-Mangion A; Zeng N, 2023, 'Neutral Tropical African CO2 Exchange Estimated From Aircraft and Satellite Observations', Global Biogeochemical Cycles, 37, http://dx.doi.org/10.1029/2023GB007804

Wikle CK; Mateu J; Zammit-Mangion A, 2023, 'Deep learning and spatial statistics', Spatial Statistics, 57, http://dx.doi.org/10.1016/j.spasta.2023.100774

Jacobson J; Cressie N; Zammit-Mangion A, 2023, 'Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data', Remote Sensing, 15, http://dx.doi.org/10.3390/rs15164038

Vu Q; Zammit-Mangion A; Chuter SJ, 2023, 'Constructing large nonstationary spatio-temporal covariance models via compositional warpings', Spatial Statistics, 54, http://dx.doi.org/10.1016/j.spasta.2023.100742

Wikle CK; Zammit-Mangion A, 2023, 'Statistical Deep Learning for Spatial and Spatiotemporal Data', Annual Review of Statistics and Its Application, 10, pp. 247 - 270, http://dx.doi.org/10.1146/annurev-statistics-033021-112628

Byrne B; Baker DF; Basu S; Bertolacci M; Bowman KW; Carroll D; Chatterjee A; Chevallier F; Ciais P; Cressie N; Crisp D; Crowell S; Deng F; Deng Z; Deutscher NM; Dubey MK; Feng S; García OE; Griffith DWT; Herkommer B; Hu L; Jacobson AR; Janardanan R; Jeong S; Johnson MS; Jones DBA; Kivi R; Liu J; Liu Z; Maksyutov S; Miller JB; Miller SM; Morino I; Notholt J; Oda T; O'Dell CW; Oh YS; Ohyama H; Patra PK; Peiro H; Petri C; Philip S; Pollard DF; Poulter B; Remaud M; Schuh A; Sha MK; Shiomi K; Strong K; Sweeney C; Té Y; Tian H; Velazco VA; Vrekoussis M; Warneke T; Worden JR; Wunch D; Yao Y; Yun J; Zammit-Mangion A; Zeng N, 2023, 'National CO2 budgets (2015-2020) inferred from atmospheric CO2 observations in support of the global stocktake', Earth System Science Data, 15, pp. 963 - 1004, http://dx.doi.org/10.5194/essd-15-963-2023

Cartwright L; Zammit-Mangion A; Deutscher NM, 2023, 'Emulation of greenhouse-gas sensitivities using variational autoencoders', Environmetrics, 34, http://dx.doi.org/10.1002/env.2754

Burr WS; Newlands NK; Zammit-Mangion A, 2023, 'Environmental data science: Part 2', Environmetrics, 34, http://dx.doi.org/10.1002/env.2788

Cressie N; Zammit-Mangion A; Jacobson J; Bertolacci M, 2023, 'Earth’s CO2 battle: a view from space', Significance, 20, pp. 14 - 19, http://dx.doi.org/10.1093/jrssig/qmad003

Zammit-Mangion A; Newlands NK; Burr WS, 2023, 'Environmental data science: Part 1', Environmetrics, 34, http://dx.doi.org/10.1002/env.2787

Ng TLJ; Zammit-Mangion A, 2023, 'Non-homogeneous Poisson process intensity modeling and estimation using measure transport', Bernoulli, 29, pp. 815 - 838, http://dx.doi.org/10.3150/22-BEJ1480

Stell AC; Bertolacci M; Zammit-Mangion A; Rigby M; Fraser PJ; Harth CM; Krummel PB; Lan X; Manizza M; Mühle J; O'Doherty S; Prinn RG; Weiss RF; Young D; Ganesan AL, 2022, 'Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion', Atmospheric Chemistry and Physics, 22, pp. 12945 - 12960, http://dx.doi.org/10.5194/acp-22-12945-2022

Vu Q; Zammit-Mangion A; Cressie N, 2022, 'MODELING NONSTATIONARY AND ASYMMETRIC MULTIVARIATE SPATIAL COVARIANCES VIA DEFORMATIONS', Statistica Sinica, 32, pp. 2071 - 2093, http://dx.doi.org/10.5705/ss.202020.0156

Ng TLJ; Zammit-Mangion A, 2022, 'Spherical Poisson point process intensity function modeling and estimation with measure transport', Spatial Statistics, 50, http://dx.doi.org/10.1016/j.spasta.2022.100629

Cressie N; Bertolacci M; Zammit-Mangion A, 2022, 'From Many to One: Consensus Inference in a MIP', Geophysical Research Letters, 49, http://dx.doi.org/10.1029/2022GL098277

Beck B; Zammit-Mangion A; Fry R; Smith K; Gabbe B, 2022, 'Spatiotemporal mapping of major trauma in Victoria, Australia', Plos One, 17, http://dx.doi.org/10.1371/journal.pone.0266521

Chuter SJ; Zammit-Mangion A; Rougier J; Dawson G; Bamber JL, 2022, 'Mass evolution of the Antarctic Peninsula over the last 2 decades from a joint Bayesian inversion', Cryosphere, 16, pp. 1349 - 1367, http://dx.doi.org/10.5194/tc-16-1349-2022

Cressie N; Sainsbury-Dale M; Zammit-Mangion A, 2022, 'Basis-Function Models in Spatial Statistics', Annual Review of Statistics and Its Application, 9, pp. 373 - 400, http://dx.doi.org/10.1146/annurev-statistics-040120-020733

Zammit-Mangion A; Bertolacci M; Fisher J; Stavert A; Rigby M; Cao Y; Cressie N, 2022, 'WOMBAT v1.0: A fully Bayesian global flux-inversion framework', Geoscientific Model Development, 15, pp. 45 - 73, http://dx.doi.org/10.5194/gmd-15-45-2022

Zammit-Mangion A; Ng TLJ; Vu Q; Filippone M, 2022, 'Deep Compositional Spatial Models', Journal of the American Statistical Association, 117, pp. 1787 - 1808, http://dx.doi.org/10.1080/01621459.2021.1887741

Vu Q; Cao Y; Jacobson J; Pearse AR; Zammit-Mangion A, 2021, 'Discussion on “Competition on Spatial Statistics for Large Datasets”', Journal of Agricultural Biological and Environmental Statistics, 26, pp. 614 - 618, http://dx.doi.org/10.1007/s13253-021-00464-0

Huang HC; Cressie N; Zammit-Mangion A; Huang G, 2021, 'False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data', Journal of Computational and Graphical Statistics, 30, pp. 1081 - 1094, http://dx.doi.org/10.1080/10618600.2021.1873144

Zammit-Mangion A; Cressie N, 2021, 'Frk: An r package for spatial and spatio-temporal prediction with large datasets', Journal of Statistical Software, 98, http://dx.doi.org/10.18637/jss.v098.i04

Zammit-Mangion A, 2020, 'Discussion on A high-resolution bilevel skew-t stochastic generator for assessing Saudi Arabia's wind energy resources', Environmetrics, 31, http://dx.doi.org/10.1002/env.2649

Zammit-Mangion A; Rougier J, 2020, 'Multi-scale process modelling and distributed computation for spatial data', Statistics and Computing, 30, pp. 1609 - 1627, http://dx.doi.org/10.1007/s11222-020-09962-6

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