Research Outputs


2. C.K. Wikle, A. Zammit-Mangion and N. Cressie (2019) Spatio-temporal Statistics with R, Boca Raton, FL: Chapman & Hall/CRC.

1. A. Zammit-Mangion, M. Dewar, V. Kadirkamanathan, A. Flesken and G. Sanguinetti (2013) Modeling Conflict Dynamics using Spatio-temporal Data, London, UK: Springer Briefs.

Journal Publications & Proceedings

44. T.L.J. Ng and A. Zammit-Mangion (2023), “Non-homogeneous Poisson process intensity modelling and estimation using measure transport,” Bernoulli, 29(1), 815-838.

43. C.K. Wikle and A. Zammit-Mangion (2022) “Statistical deep learning for spatial and spatio-temporal data,” in press with Annual Review of Statistics and Its Application.

42. L. Cartwright, A. Zammit-Mangion, and N. Deutscher (2022) “Emulation of greenhouse-gas sensitivities using variational autoencoders,” in press with Environmetrics.

41. A. Zammit-Mangion, T.L.J. Ng, Q. Vu and M. Filippone (2022) “Deep compositional spatial models,” in press with Journal of the American Statistical Association, doi:10.1080/01621459.2021.1887741.

40. A.C. Stell, M. Bertolacci, A. Zammit-Mangion, M. Rigby, P. J. Fraser, C. M. Harth, P. B. Krummel, X. Lan, M. Manizza, J. Mühle, S. O’Doherty, R. G. Prinn, R. F. Weiss, D. Young, and A. L. Ganesan (2022) “Understanding the growth of atmospheric nitrous oxide using a global hierarchical inversion,” Atmospheric Chemistry and Physics, 22, 12945–12960.

39. B. Beck, A. Zammit-Mangion, R. Fry, K. Smith, and B. Gabbe (2022), “Spatiotemporal mapping of major trauma in Victoria, Australia,” PLOS One, 17(7):e0266521.

38. N. Cressie, M. Bertolacci, and A. Zammit-Mangion (2022), “From many to one: Consensus inference in a MIP,” Geophysical Research Letters, 49, e2022GL098277.

37. S. Chuter, A. Zammit-Mangion, J. Rougier, G. Dawson and J.L. Bamber (2022) “Mass evolution of the Antarctic Peninsula over the last two decades from a joint Bayesian inversion,” The Cryosphere, 16, 1349–1367.

36. N. Cressie, M. Sainsbury-Dale and A. Zammit-Mangion (2022) “Basis-function models in spatial statistics,” Annual Review of Statistics and Its Application, 9(1), 373-400.

35. T.L.J. Ng and A. Zammit-Mangion (2022) “Spherical Poisson point process intensity function modeling and estimation with measure transport,” Spatial Statistics, 50, 100629.

34. Q. Vu, A. Zammit-Mangion and N. Cressie (2022) “Modeling nonstationary and asymmetric multivariate spatial covariances via deformations,” Statistica Sinica, 32, 2071-2093.

33. A. Zammit-Mangion, M. Bertolacci, J. Fisher, A. Stavert, M. Rigby, Y. Cao and N. Cressie (2022) “WOMBAT: A fully Bayesian global flux-inversion framework,” Geophysical Model Development, 15, 45-73.

32. H.-C. Huang, N. Cressie, A. Zammit-Mangion and G. Huang (2021) “False discovery rates to detect signals from incomplete spatially aggregated data,” Journal of Computational and Graphical Statistics, 30(4), 1081-1094.

A. Zammit-Mangion and N. Cressie (2021) “FRK: An R package for spatial and spatio-temporal prediction with large datasets,” Journal of Statistical Software, 98(4), 1-48.

30. A. Zammit-Mangion and J. Rougier (2020) “Multi-scale process modelling and distributed computation for spatial data,” Statistics and Computing , 30(6), 1609-1627.

29. A. Zammit-Mangion and C.K. Wikle (2020) “Deep integro-difference equation models for spatio-temporal forecasting,” Spatial Statistics , 37, 10048.

28. E. Yoo, A. Zammit-Mangion and M. Chipeta (2020) “Adaptive spatial sampling design for environmental field prediction using low-cost sensing technologies,” Atmospheric Environment , 221, 117091.

27. T. Suesse and A. Zammit-Mangion (2019), “Marginal maximum likelihood estimation of conditional autoregressive models with missing data,” Stat, 8, e226.

26. M.J. Heaton, A. Datta, A. Finley, R. Furrer, J. Guinness, R. Guhaniyogi, F. Gerber, R.B. Gramacy, D. Hammerling, M. Katzfuss, F. Lindgren, D.W. Nychka, F. Sun and A. Zammit-Mangion (2019), “A Case Study Competition among Methods for Analyzing Large Spatial Data,” Journal of Agricultural, Biological, and Environmental Statistics, 24, pp. 398-425.

25. L. Cartwright, A. Zammit-Mangion, S. Bhatia, I. Schroder, F. Phillips, T. Coates, K. Negandhi, T. Naylor, M. Kennedy, S. Zegelin, N. Wokker, N. M. Deutscher and A. Feitz (2019) “Bayesian atmospheric tomography for detection and quantification of methane emissions: Application to data from the 2015 Ginninderra release experiment,” Atmospheric Measurement Techniques, 12, pp. 4659–4676.

24. A. Zammit-Mangion and J.C. Rougier (2018), “A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields,” Computational Statistics & Data Analysis, 123, pp. 116-130.

23. A. Zammit-Mangion, N. Cressie and C. Shumack (2018) “On statistical approaches to generate Level 3 products from statistical remote sensing retrievals,” Remote Sensing, 10(1), 155.

22. A. Martin-Español, J.L. Bamber, an A. Zammit-Mangion (2017) “Constraining the mass balance of East Antarctica,” Geophysical Research Letters, 44(9), pp. 4168–4175.

21. T. Suesse and A. Zammit-Mangion (2017) “Computational aspects of the EM algorithm for spatial econometric models with missing data,” Journal of Statistical Computing and Simulation, 87(9), pp. 1767–1786.

20. B. Cseke, A. Zammit-Mangion, G. Sanguinetti and T. Heskes (2016) “Sparse approximations in spatio-temporal point-process models,” Journal of the American Statistical Association, 111(516), pp. 1746–1763.

19. A. Martín-Español, M.A. King, A. Zammit-Mangion, S.B. Andrews, P. Moore and J.L. Bamber (2016) “An assessment of forward and inverse GIA solutions for Antarctica,” Journal of Geophysical Research: Solid Earth, 121(9), pp. 6947–6965.

18. N. Cressie and A. Zammit-Mangion (2016) “Multivariate spatial covariance models: A conditional approach,” Biometrika, 103(4), pp. 915–935.

17. J.C. Rougier and A. Zammit-Mangion (2016) “Visualisation for large-scale Gaussian updates,” Scandinavian Journal of Statistics, 43(4), pp. 1153–1161.

16. A. Zammit-Mangion, N. Cressie and A.L. Ganesan (2016) “Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion,” Spatial Statistics, 18(A), pp. 194–220.

15. A. Martín-Español, A. Zammit-Mangion, P.J. Clarke, T. Flament, V. Helm, M.A. King, S.B. Luthcke, E. Petrie, F. Remy, N. Schoen, B. Wouters and J.L. Bamber (2016) “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,” Journal of Geophysical Research: Earth Surface, 121(2), pp. 182–200.

14. A. Zammit-Mangion, N. Cressie, A.L. Ganesan, S. O’Doherty and A.J. Manning (2015) “Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion,” Chemometrics and Intelligent Laboratory Systems, 149(B), pp. 227–241.

13.A. Zammit-Mangion, J. Bamber, N. Schoen and J.C. Rougier (2015) “A data-driven approach for assessing ice-sheet mass balance in space and time,” Annals of Glaciology, 56(70). pp. 175–183.

12. N. Schoen, A. Zammit-Mangion, J.C. Rougier, T. Flament, F. Remy, S. Luthcke and J. Bamber (2015) “Simultaneous solution for mass trends on the West Antarctic Ice Sheet,” The Cryosphere, 9, pp. 805–819.

11. A. Zammit-Mangion, J.C. Rougier, N. Schoen, F. Lindgren and J. Bamber (2015) “Multivariate spatio-temporal modelling for assessing Antarctica’s present-day contribution to sea-level rise,” Environmetrics, 26(3), pp. 159–177.

10. A. Zammit-Mangion, J. Rougier, J. Bamber and N. Schoen (2014) “Resolving the Antarctic contribution to sea-level rise: a hierarchical modelling framework,” Environmetrics, 25(4), pp. 245–264.

9. A.L. Ganesan, M. Rigby, A. Zammit-Mangion, A.J. Manning, R.G. Prinn, P.J. Fraser, C.M. Harth, K.-R. Kim, P.B. Krummel, S. Li, J. Mühle, S.J. O’Doherty, S. Park, P.K. Salameh, L.P. Steele and R.F. Weiss (2014) “Characterization of uncertainties in trace gas inversions using hierarchical Bayesian methods,” Atmospheric Chemistry and Physics, 14, pp. 3855–3864.

8. R.I. Menzies, A. Zammit-Mangion, L. Hollis, R. Lennen, M.A. Jansen, D.J. Webb, J.J. Mullins, J.W. Dear, G. Sanguinetti and M. Bailey (2013) “An anatomically unbiased approach of analysis of renal BOLD magnetic resonance images,” American Journal of Physiology: Renal Physiology, 305(6), pp. F845–52.

7. A. Zammit Mangion, M. Dewar, V. Kadirkamanathan and G. Sanguinetti (2012) “Point process modelling of the Afghan War Diary,” PNAS, 109(31), pp. 12414–12419. Awarded the Cozzarelli Prize from the National Academy of Sciences.

6. A. Zammit Mangion, G. Sanguinetti and V. Kadirkamanathan (2012) “Variational estimation in spatio-temporal systems from continuous and point process observations,” IEEE Transactions on Signal Processing, 60(7), pp. 3449–3459.

5. A. Zammit Mangion, K. Yuan, V. Kadirkamanathan, M. Niranjan and G. Sanguinetti (2011) “Online variational inference of state-space models with point process observations,” Neural Computation, 23(8), pp. 1967–1999.

4. A. Zammit Mangion, G. Sanguinetti and V. Kadirkamanathan (2011) “A variational approach for the online dual estimation of spatio-temporal systems governed by the IDE,” Proceedings of the 18th IFAC World Congress, 18(1), pp. 3204–3209.

3. A. Zammit Mangion, S. Anderson and V. Kadirkamanathan (2011) “Exploration and control of stochastic spatio-temporal systems with mobile agents,” Proceedings of the 18th IFAC World Congress, 18(1), pp. 4489–4494.

2. A.R. Mills, B. Apopei, A. Zammit Mangion, H. Barron-Gonzales, P. Gunetti, H. A. Thompson and P. Garbett (2010) “Heterogeneous hardware technologies for accelerating complex aerospace system simulations,” Proceedings of the IEEE Aerospace Conference, doi:10.1109/AERO.2010.5446789.

1. A. Zammit[-]Mangion and S.G. Fabri (2008) “Experimental Evaluation of Haptic Control for Human Activated Command Devices,” Proceedings of the UKACC Control Conference, available online at Awarded prize for best student paper.

Other Peer-Reviewed Publications

4. A. Zammit-Mangion “Comments on: A high-resolution bilevel skew-t stochastic generator for assessing Saudi Arabia’s wind energy resources,” Environmetrics, e2649, doi:10.1002/env.2649 Wiley StatsRef: Statistics Reference Online, doi: 10.1002/9781118445112.stat07717.pub2.

3. N. Cressie, S. Burden, C. Shumack, A. Zammit-Mangion and B. Zhang (2017) “Environmental Informatics,” Wiley StatsRef: Statistics Reference Online, doi: 10.1002/9781118445112.stat07717.pub2.

2. C.K. Wikle, N. Cressie, A. Zammit-Mangion and C. Shumack (2017) “A common task framework for objective comparison of spatial prediction methodologies.” Statistics Views, Wiley, Online (peer- reviewed)

1. N. Cressie, S. Burden, W. Davis, P.N. Krivitsky, P. Mokhtarian, T. Suesse and A. Zammit-Mangion (2015) “Capturing multivariate spatial dependence: Model, estimate, and then predict,” Statistical Science, 30(2), pp. 170–175.

Non-Peer-Reviewed Publications

3. Q. Vu, Y. Cao, J. Jacobson, A. R. Pearse and A. Zammit-Mangion (2021) “Discussion on ‘Competition on Spatial Statistics for Large Datasets’,” Journal of Agricultural, Biological, and Environmental Statistics,

2. A. Zammit-Mangion (2019) Comment on “Visualizing spatiotemporal models with virtual reality: from fully immersive environments to applications in stereoscopic view,” by S. Castruccio, M.G. Genton, and Y. Sun. Journal of the Royal Statistical Society, Series A, 182, 429 (2019).

1. A. Zammit-Mangion (2017) “Discussion of ‘Barycentres and hurricane trajectories’ in Geometry driven statistics, 146–160, Wiley Ser. Probab. Stat., Wiley, Chichester, 2015,” AMS Reviews, AMS, Online: mathscinet-getitem?mr=3616211.

Open-Source Software


IDE, an R package for carrying out spatio-temporal modelling with the integro-difference equation. Available on CRAN.


FRK, an R package for carrying out spatial and spatio-temporal fixed rank kriging on massive datasets. Available on CRAN.


atminv, an R package for reproducing results in the article “Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion.” Available on github.


bicon, an R package for reproducing results in the article “Multivariate Spatial Covariance Models: A Conditional Approach.” Available on github.


EFDR, an R package for Enhanced False Discovery Rate for signal detection in noisy images. Available on CRAN.


MVST, an R package for modelling and predicting with multivariate spatio-temporal random fields when the covariance parameters are known. Available on github.


MATLAB function for Gaussian mixture modelling with background noise. Available on MATLAB Central.