Publications

Zamani, A., Haghbin H., Hashemi M., & Hyndman R. J. (In Press).  Seasonal functional autoregressive models. Journal of Time Series Analysis. doi: 10.1111/jtsa.12608
Dissanayake, T., Fernando T., Denman S., Sridharan S., & Fookes C. (2022).  Geometric Deep Learning for Subject-Independent Epileptic Seizure Prediction using Scalp EEG Signals. IEEE Journal of Biomedical and Health Informatics. 26(2), 527-538. doi: 10.1109/JBHI.2021.3100297
Dokumentov, A., & Hyndman R. J. (2022).  STR: Seasonal-Trend decomposition using Regression. INFORMS Journal on Data Science. 1(1), 50-62. doi: 10.1287/ijds.2021.0004
Jiang, J., Wand M. P., & Bhaskaran A. (2022).  Usable and precise asymptotics for generalized linear mixed model analysis and design. Journal of the Royal Statistical Society, Series B. 84(1), 55-82. doi: 10.1111/rssb.12473
Kandanaarachchi, S., & Hyndman R. J. (2022).  Leave-one-out kernel density estimates for outlier detection. Journal of Computational and Graphical Statistics. 31(2), 586-599. doi: 10.1080/10618600.2021.2000425
Laa, U., Cook D., & Lee S. (2022).  Burning Sage: Reversing the Curse of Dimensionality in the Visualization of High-Dimensional Data. Journal of Computational and Graphical Statistics. 31(1), 40-49. doi: 10.1080/10618600.2021.1963264
Rostami-Tabar, B., Ali M. M., Hong T., Hyndman R. J., Porter M. D., & Syntetos A. (2022).  Forecasting for social good. International Journal of Forecasting. 38(3), 1245-1257. doi: 10.1016/j.ijforecast.2021.02.010
Aswi, A., Sukarna, Cramb S., & Mengersen K. (2021).  Effects of Climatic Factors on Dengue Incidence: A Comparison of Bayesian Spatio-Temporal Models. Journal of Physics: Conference Series. 1863(1), 012050. doi: 10.1088/1742-6596/1863/1/012050
Aswi, A., Cramb S., Duncan E., & Mengersen K. (2021).  Detecting Spatial Autocorrelation for a Small Number of Areas: a practical example. Journal of Physics: Conference Series. 1899(1), 012098. doi: 10.1088/1742-6596/1899/1/012098
Ben Taieb, S., Taylor J. W., & Hyndman R. J. (2021).  Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data. Journal of the American Statistical Association. 116(533), 27-43. doi: 10.1080/01621459.2020.1736081
Cui, T., & Zahm O. (2021).  Data-free likelihood-informed dimension reduction of Bayesian inverse problems. Inverse Problems. 37(4), 045009. doi: 10.1088/1361-6420/abeafb
Dawkins, L. C., Williamson D. B., Mengersen K. L., Morawska L., Jayaratne R., & Shaddick G. (2021).  Where Is the Clean Air? A Bayesian Decision Framework for Personalised Cyclist Route Selection Using R-INLA. Bayesian Analysis. 16(1), 61-91. doi: 10.1214/19-BA1193
de Gunst, M., Hautphenne S., Mandjes M., & Sollie B. (2021).  Parameter estimation for multivariate population processes: a saddlepoint approach. Stochastic Models. 37(1), 168 - 196. doi: 10.1080/15326349.2020.1832895
Dufays, A., Li Z., Rombouts J. V. K., & Song Y. (2021).  Sparse change‐point VAR models. Journal of Applied Econometrics. 36(6), 703-727. doi: 10.1002/jae.2844
Eckert, F., Hyndman R. J., & Panagiotelis A. (2021).  Forecasting Swiss Exports using Bayesian Forecast Reconciliation. European Journal of Operational Research. 291(2), 693-710. doi: 10.1016/j.ejor.2020.09.046
Forrester, P., & Zhang J. (2021).  Corank-1 projections and the randomised Horn problem. Tunisian Journal of Mathematics. 3(1), 55 - 73. doi: 10.2140/tunis.2021.3.55
Gunawan, D., Griffiths W., & Chotikapanich D. (2021).  Posterior probabilities for Lorenz and stochastic dominance of Australian income distributions. Economic Record. 97(319), 504-524. doi: 10.1111/1475-4932.12628
Gundry, L., Guo S-X., Kennedy G., Keith J., Robinson M., Gavaghan D., et al. (2021).  Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry. Chemical Communications. 57(15), 1855-1870. doi: 10.1039/D0CC07549C
Gundry, L., Kennedy G., Keith J., Robinson M., Gavaghan D., Bond A. M., et al. (2021).  A Comparison of Bayesian Inference Strategies for Parameterisation of Large Amplitude AC Voltammetry Derived from Total Current and Fourier Transformed Versions. ChemElectroChem. 8(12), 2238-2258. doi: 10.1002/celc.202100391
Hyndman, R. J., Zeng Y., & Shang H. Lin (2021).  Forecasting the old‐age dependency ratio to determine a sustainable pension age. Australian & New Zealand Journal of Statistics. 63(2), 241-256. doi: 10.1111/anzs.12330
Li, H., & Hyndman R. J. (2021).  Assessing mortality inequality in the U.S.: What can be said about the future?. Insurance: Mathematics and Economics. 99, 152-162. doi: 10.1016/j.insmatheco.2021.03.014
Menictas, M.., Nolan T.H.., Simpson D.G.., & Wand M. (2021).  Streamlined variational inference for higher level group-specific curve models. Statistical Modelling. 21(6), 479-519. doi: 10.1177/1471082X20930894
Miller, M. E., Motti C. A., Menéndez P., & Kroon F. J. (2021).  Efficacy of Microplastic Separation Techniques on Seawater Samples: Testing Accuracy Using High-Density Polyethylene. The Biological Bulletin. 240(1), 52 - 66. doi: 10.1086/710755
Morota, G., Cheng H., Cook D., & Tanaka E. (2021).  Prospects for interactive and dynamic graphics in the era of data-rich animal science. Journal of Animal Science. 99(2), skaa402. doi: 10.1093/jas/skaa402
Nadarajah, K.., Martin G. M., & Poskitt D.S.. (2021).  Optimal bias correction of the log-periodogram estimator of the fractional parameter: A jackknife approach. Journal of Statistical Planning and Inference. 211, 41 - 79. doi: 10.1016/j.jspi.2020.04.010
Snook, D. W., Kleinmann S. M., White G., & Horgan J. G. (2021).  Conversion motifs among Muslim converts in the United States.. Psychology of Religion and Spirituality. 13(4), 482 - 492. doi: 10.1037/rel0000276
Spiliotis, E., Abolghasemi M., Hyndman R. J., Petropoulos F., & Assimakopoulos V. (2021).  Hierarchical forecast reconciliation with machine learning. Applied Soft Computing. 112, 107756. doi: 10.1016/j.asoc.2021.107756
Thamrin, S. Astuti, Aswi, Ansariadi, Jaya A. Kresna, & Mengersen K. (2021).  Bayesian spatial survival modelling for dengue fever in Makassar, Indonesia. Gaceta Sanitaria. 35(S1), S59 - S63. doi: 10.1016/j.gaceta.2020.12.017
Tran, M.-N.., Scharth M.., Gunawan D.., Kohn R.., Brown S.. D., & Hawkins G.. E. (2021).  Robustly estimating the marginal likelihood for cognitive models via importance sampling. Behavior Research Methods. 53(3), 1148 - 1165. doi: 10.3758/s13428-020-01348-w
Vaisman, R. (2021).  Sequential stratified splitting for efficient Monte Carlo integration. Sequential Analysis. 40(3), 314 - 335. doi: 10.1080/07474946.2021.1940493
Zaloumis, S. G., Whyte J. M., Tarning J., Krishna S., McCaw J. M., Cao P., et al. (2021).  Development and validation of an in silico decision-tool to guide optimisation of intravenous artesunate dosing regimens for severe falciparum malaria patients. Antimicrobial Agents and Chemotherapy. 65(6), e02346-20. doi: 10.1128/AAC.02346-20
Adams, M. P., Koh E. J. Y., Vilas M. P., Collier C. J., Lambert V. M., Sisson S. A., et al. (2020).  Predicting seagrass decline due to cumulative stressors. Environmental Modelling & Software. 130, 104717. doi: 10.1016/j.envsoft.2020.104717
Broc, C., Calvo B., & Liquet B. (2020).  Penalized Partial Least Square applied to structured data. Arabian Journal of Mathematics. 9, 329-344. doi: 10.1007/s40065-019-0248-6
Costa, A. Goncalves, Nielsen T., Dal Grande E., Tuke J., & Hazel S. (2020).  Regulatory Compliance in Online Dog Advertisements in Australia. Animals. 10(3), 425. doi: 10.3390/ani10030425
Damjanovic, K., Menéndez P., Blackall L. L., & van Oppen M. J. H. (2020).  Early Life Stages of a Common Broadcast Spawning Coral Associate with Specific Bacterial Communities Despite Lack of Internalized Bacteria. Microbial Ecology. 79(3), 706 - 719. doi: 10.1007/s00248-019-01428-1
Damjanovic, K., Blackall L. L., Menéndez P., & van Oppen M. J. H. (2020).  Bacterial and algal symbiont dynamics in early recruits exposed to two adult coral species. Coral Reefs. 39(1), 189 - 202. doi: 10.1007/s00338-019-01871-z
Forbes, J., Cook D., & Hyndman R. J. (2020).  Spatial modelling of the two‐party preferred vote in Australian federal elections: 2001–2016. Australian & New Zealand Journal of Statistics. 62(2), 168 - 185. doi: 10.1111/anzs.12292
Fu, L., Wang Y-G., & Cai F. (2020).  A working likelihood approach for robust regression. Statistical Methods in Medical Research. 29(12), 3641 - 3652. doi: 10.1177/0962280220936310
González-Rivero, M., Beijbom O., Rodriguez-Ramirez A., Bryant D. E. P., Ganase A., Gonzalez-Marrero Y., et al. (2020).  Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach. Remote Sensing. 12(3), 489. doi: 10.3390/rs12030489
Hall, P.., Johnstone I.M.., Ormerod J.T.., Wand M., & Yu J.C.F.. (2020).  Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation. Journal of the American Statistical Association. 115(532), 1902-1916. doi: 10.1080/01621459.2019.1665529
Lander, D., Gunawan D., Griffiths W., & Chotikapanich D. (2020).  Bayesian assessment of Lorenz and stochastic dominance. Canadian Journal of Economics. 53(2), 767-799. doi: 10.1111/caje.12443
Lee, S., Zhang A. Y., Su S., Ng A. P., Holik A. Z., Asselin-Labat M-L., et al. (2020).  Covering all your bases: incorporating intron signal from RNA-seq dataAbstract. NAR Genomics and Bioinformatics. 2(3), lqaa073. doi: 10.1093/nargab/lqaa073
Lee, S., Lawrence M., & Love M. I. (2020).  Fluent genomics with plyranges and tximeta. F1000Research. 9, 109. doi: 10.12688/f1000research.22259.1
Makridakis, S., Hyndman R. J., & Petropoulos F. (2020).  Forecasting in social settings: The state of the art. International Journal of Forecasting. 36(1), 15 - 28. doi: 10.1016/j.ijforecast.2019.05.011
Mitchell, D., Ye N., & De Sterck H. (2020).  Nesterov acceleration of alternating least squares for canonical tensor decomposition: Momentum step size selection and restart mechanisms. Numerical Linear Algebra with Applications. 27(4), e2297. doi: 10.1002/nla.2297
Mohajerpoor, R., Saberi M., Vu H. L., Garoni T. M., & Ramezani M. (2020).  $H_{\inf}$ robust perimeter flow control in urban networks with partial information feedback. Transportation Research Part B: Methodological. 137, 47-73. doi: 10.1016/j.trb.2019.03.010
Montero-Manso, P., Athanasopoulos G., Hyndman R. J., & Talagala T. S. (2020).  FFORMA: Feature-based forecast model averaging. International Journal of Forecasting. 36(1), 86 - 92. doi: 10.1016/j.ijforecast.2019.02.011
Moores, M., Nicholls G., Pettitt A., & Mengersen KL. (2020).  Scalable Bayesian Inference for the Inverse Temperature of a Hidden Potts Model. Bayesian Analysis. 15(1), 1-27. doi: 10.1214/18-BA1130
Pham, D-T., Ristic R., Stockdale V. J., Jeffery D. W., Tuke J., & Wilkinson K. (2020).  Influence of partial dealcoholization on the composition and sensory properties of Cabernet Sauvignon wines. Food Chemistry. 325, 126869. doi: 10.1016/j.foodchem.2020.126869
Schaffter, T., Buist D. S. M., Lee C. I., Nikulin Y., Ribli D., Guan Y., et al. (2020).  Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Network Open. 3(3), e200265. doi: 10.1001/jamanetworkopen.2020.0265
Schlaff, A., Menendez P., Hall M., Heupel M., Armstrong T., & Motti C. (2020).  Acoustic tracking of a large predatory marine gastropod, Charonia tritonis, on the Great Barrier Reef. Marine Ecology Progress Series. 642, 147-161. doi: 10.3354/meps13291
Senarathne, S.. G. J., Drovandi C.. C., & McGree J.. M. (2020).  Bayesian sequential design for Copula models. TEST. 29, 454-478. doi: 10.1007/s11749-019-00661-7
Wang, E., Cook D., & Hyndman R. J. (2020).  Calendar-Based Graphics for Visualizing People’s Daily Schedules. Journal of Computational and Graphical Statistics. 29(3), 490 - 502. doi: 10.1080/10618600.2020.1715226
Wang, E., Cook D., & Hyndman R. J. (2020).  A New Tidy Data Structure to Support Exploration and Modeling of Temporal Data. Journal of Computational and Graphical Statistics. 29(3), 466-478. doi: 10.1080/10618600.2019.1695624
Wickramasuriya, S. L., Turlach B. A., & Hyndman R. J. (2020).  Optimal non-negative forecast reconciliation. Statistics and Computing. 30(5), 1167 - 1182. doi: 10.1007/s11222-020-09930-0
Wilson-Stewart, K. S., Fontanarosa D., Li D., Drovandi C. C., Anderson R. K., & Trapp J. V. (2020).  Taller staff occupationally exposed to less radiation to the temple in cardiac procedures, but risk higher doses during vascular cases. Scientific Reports. 10, 16103. doi: 10.1038/s41598-020-73101-4
Ayyer, A., Finn C., & Roy D. (2019).  The Phase Diagram for a Multispecies Left-Permeable Asymmetric Exclusion Process. Journal of Statistical Physics. 174(3), 605-621. doi: 10.1007/s10955-018-2183-x
Barbour, A.. D., Roellin A., & Ross N. (2019).  Error bounds in local limit theorems using Stein's method. Bernoulli. 25(2), 1076-1104. doi: 10.3150/17-BEJ1013
Benton, M. C., Lea R. A., Macartney-Coxson D., Sutherland H. G., White N., Kennedy D., et al. (2019).  Genome-wide allele-specific methylation is enriched at gene regulatory regions in a multi-generation pedigree from the Norfolk Island isolate. Epigenetics & Chromatin. 12, 60. doi: 10.1186/s13072-019-0304-7
Bilal, A., Rextin A., Kakakhel A., & Nasim M. (2019).  Analyzing Emergent Users’ Text Messages Data and Exploring Its Benefits. IEEE Access. 7, 2870 - 2879. doi: 10.1109/ACCESS.2018.2885332
Braunsteins, P., Decrouez G., & Hautphenne S. (2019).  A pathwise approach to the extinction of branching processes with countably many types. Stochastic Processes and their Applications. 129(3), 713-739. doi: 10.1016/j.spa.2018.03.013
Chakravorty, D., Banerjee K., Mapder T., & Saha S. (2019).  In silico modeling of phosphorylation dependent and independent c-Myc degradation. BMC Bioinformatics. 20, 230. doi: 10.1186/s12859-019-2846-x
Chew, J. S. C., Zhang L., & Gan H. S. (2019).  Optimizing limited-stop services with vehicle assignment. Transportation Research Part E: Logistics and Transportation Review. 129, 228 - 246. doi: 10.1016/j.tre.2019.08.001
Cope, R. C., Ross J.V., Wittmann T. A., Watts M. J., & Cassey P. (2019).  Predicting the Risk of Biological Invasions Using Environmental Similarity and Transport Network Connectedness. Risk Analysis. 39(1), 35-53. doi: 10.1111/risa.12870
Creutzig, T., Kanade S., Liu T., & Ridout D. (2019).  Cosets, characters and fusion for admissible-level. Nuclear Physics B. 938, 22 - 55. doi: 10.1016/j.nuclphysb.2018.10.022
Dasgupta, P., Whop L. J., Diaz A., Cramb S., Moore S. P., Brotherton J. M. L., et al. (2019).  Spatial variation in cervical cancer screening participation and outcomes among Indigenous and non-Indigenous Australians in Queensland. Geographical Research. 57(1), 111-122. doi: 10.1111/1745-5871.12281
de Micheaux, P. Lafaye, Liquet B., & Sutton M. (2019).  PLS for Big Data: A unified parallel algorithm for regularised group PLS. Statistics Surveys. 13, 119-149. doi: 10.1214/19-SS125
Ding, Z., Chen B., Zhang L., Jiang R., Wu Y., & Ding J. (2019).  Segment travel time route guidance strategy in advanced traveler information systems. Physica A: Statistical Mechanics and its Applications. 534, 120432. doi: 10.1016/j.physa.2019.01.001
Feroz, F., Hobson M. P., Cameron E., & Pettitt A. N. (2019).  Importance Nested Sampling and the MultiNest Algorithm. The Open Journal of Astrophysics. 2(1), 11120. doi: 10.21105/astro.1306.2144
Gunawan, D.., Tran M.-N.., Suzuki K.., Dick J.., & Kohn R. (2019).  Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins. Statistics and Computing. 29(5), 933-946. doi: 10.1007/s11222-018-9846-y
Haller-Bull, V., & Bode M. (2019).  Superadditive and subadditive dynamics are not inherent to the types of interacting threat. (Hewitt, J., Ed.).PLOS ONE. 14(8), e0211444. doi: 10.1371/journal.pone.0211444
Harris, D., Martin G. M., Perera I., & Poskitt D.. S. (2019).  Construction and Visualization of Confidence Sets for Frequentist Distributional Forecasts. Journal of Computational and Graphical Statistics. 28(1), 92-104. doi: 10.1080/10618600.2018.1476252
ISERLES, A.., & MACNAMARA S.. (2019).  Applications of Magnus expansions and pseudospectra to Markov processes. European Journal of Applied Mathematics. 30(2), 400-425. doi: 10.1017/S0956792518000177
Jiang, Y., Wang Y-G., Fu L., & Wang X. (2019).  Robust Estimation Using Modified Huber’s Functions With New Tails. Technometrics. 61(1), 111-122. doi: 10.1080/00401706.2018.1470037
Laub, P. J., Salomone R., & Botev Z. I. (2019).  Monte Carlo estimation of the density of the sum of dependent random variables. Mathematics and Computers in Simulation. 161, 23-31. doi: 10.1016/j.matcom.2018.12.001
Mapder, T., Clifford S., Aaskov J., & Burrage K. (2019).  A population of bang-bang switches of defective interfering particles makes within-host dynamics of dengue virus controllable. (Papin, J. A., Ed.).PLOS Computational Biology. 15(11), e1006668. doi: 10.1371/journal.pcbi.1006668
Martin, G. M., McCabe B. P. M., Frazier D. T., Maneesoonthorn W., & Robert C. P. (2019).  Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models. Journal of Computational and Graphical Statistics. 28(3), 508-522. doi: 10.1080/10618600.2018.1552154
Ockelford, A., Woodcock S., & Haynes H. (2019).  The impact of inter‐flood duration on non‐cohesive sediment bed stability. Earth Surface Processes and Landforms. 44(14), 2861 - 2871. doi: 10.1002/esp.4713
Padgham, M., Boeing G., Cooley D., Tierney N., Sumner M., Phan T. G., et al. (2019).  An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services. Frontiers in Neurology. 10, 743. doi: 10.3389/fneur.2019.00743
Parsons, S.., Fuller S.., Peterson E., & Doohan B.. (2019).  The sound of management: Acoustic monitoring for agricultural industries. Ecological Indicators. 96(Part 1), 739-746. doi: 10.1016/j.ecolind.2018.09.029
Pham, D-T., Stockdale V. J., Jeffery D. W., Tuke J., & Wilkinson K. L. (2019).  Investigating Alcohol Sweetspot Phenomena in Reduced Alcohol Red Wines. Foods. 8(10), 491. doi: 10.3390/foods8100491
Quiroz, M., Kohn R., Villani M., & Tran M N. (2019).  Speeding Up MCMC by Efficient Data Subsampling. Journal of the American Statistical Association. 114(526), 831-843. doi: 10.1080/01621459.2018.1448827
Sarker, C., Mejias L., Maire F., & Woodley A. (2019).  Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information. Remote Sensing. 11(19), 2331. doi: 10.3390/rs11192331
Sharp, J. A., Browning A. P., Mapder T., Burrage K., & Simpson M. J. (2019).  Optimal control of acute myeloid leukaemia. Journal of Theoretical Biology. 470, 30–42. doi: 10.1016/j.jtbi.2019.03.006
Sutton, M., Mengersen KL., & Liquet B.. (2019).  [HDDA] sparse subspace constrained partial least squares. Journal of Statistical Computation and Simulation. 89(6), 1005-1019. doi: 10.1080/00949655.2018.1555830
Van Looy, K., Tonkin J. D., Floury M., Leigh C., Soininen J., Larsen S., et al. (2019).  The three Rs of river ecosystem resilience: Resources, recruitment, and refugia. River Research and Applications. 35(2), 107 - 120. doi: 10.1002/rra.3396
Varney, J., Bean N. G., & Mackay M. (2019).  The self-regulating nature of occupancy in ICUs: stochastic homoeostasis. Health Care Management Science. 22(4), 615-634. doi: 10.1007/s10729-018-9448-4
Weerasinghe, HN., Burrage PM.,, & Nicolau DV. (2019).  Mathematical Models of Cancer Cell Plasticity. Journal of Oncology . 2019, 2403483. doi: 10.1155/2019/2403483
Wickramasuriya, S. L., Athanasopoulos G., & Hyndman R. J. (2019).  Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization. Journal of the American Statistical Association. 114(526), 804-819. doi: 10.1080/01621459.2018.1448825
Wu, J., Cui Z., Chen Y., Kong D., & Wang Y-G. (2019).  A new hybrid model to predict the electrical load in five states of Australia. Energy. 166, 598 - 609. doi: 10.1016/j.energy.2018.10.076
Zhang, X., Wen F., & De Gier J. (2019).  T-Q relations for the integrable two-species asymmetric simple exclusion process with open boundaries. Journal of Statistical Mechanics: Theory and Experiment. 2019(1), 014001. doi: 10.1088/1742-5468/aaeb4a
Adjeroud, M., Kayal M., Iborra-Cantonnet C., Vercelloni J., Bosserelle P., Liao V., et al. (2018).  Recovery of coral assemblages despite acute and recurrent disturbances on a South Central Pacific reef. Scientific Reports. 8(1), 9680. doi: 10.1038/s41598-018-27891-3
Agius, A., Morelato M., Moret S., Chadwick S., Jones K., Epple R., et al. (2018).  Using handwriting to infer a writer’s country of origin for forensic intelligence purposes. Forensic Science International. 282, 144 - 156. doi: 10.1016/j.forsciint.2017.11.028
Auger, J., Creutzig T., & Ridout D. (2018).  Modularity of logarithmic parafermion vertex algebras. Letters in Mathematical Physics. 108(12), 2543 - 2587. doi: 10.1007/s11005-018-1098-4
Ayyer, A., Finn C., & Roy D. (2018).  Matrix product solution of a left-permeable two-species asymmetric exclusion process. Physical Review E. 97(1),  doi: 10.1103/PhysRevE.97.012151
Baatar, D., Ehrgott M., Hamacher H. W., & Raschendorfer I. M. (2018).  Minimizing the number of apertures in multileaf collimator sequencing with field splitting. Discrete Applied Mathematics. 250, 87-103. doi: 10.1016/j.dam.2018.04.016
Baffour, B., Silva D., Veiga A., Sexton C., & Brown J. (2018).  Small area estimation strategy for the 2011 Census in England and Wales. Statistical Journal of the IAOS. 34(3), 395 - 407. doi: 10.3233/SJI-180427
Bagrow, J. P., & Mitchell L. (2018).  The quoter model: A paradigmatic model of the social flow of written information. Chaos: An Interdisciplinary Journal of Nonlinear Science. 28(7), 075304. doi: 10.1063/1.5011403
Bean, N. G., Latouche G., & Taylor P. (2018).  Physical Interpretations for Quasi-Birth-and-Death Process Algorithms. Queueing Models and Service Management. 1(2), 59-78.
Bellsky, T., & Mitchell L. (2018).  A shadowing-based inflation scheme for ensemble data assimilation. Physica D: Nonlinear Phenomena. 380-381, 1 - 7. doi: 10.1016/j.physd.2018.05.002
Borg, D.N.., Stewart I.B.., Costello J.T.., Drovandi C.C.., & Minett G.M.. (2018).  The impact of environmental temperature deception on perceived exertion during fixed-intensity exercise in the heat in trained-cyclists. Physiology & Behavior. 194, 333 - 340. doi: 10.1016/j.physbeh.2018.06.026
Broc, C.., Evangelou M.., Guenel P.., Truing T.., & Liquet B.. (2018).  Investigating Gene- and Pathway-environment Interaction analysis approaches. Journal de la Société Française de Statistique. 159(2), 56-83.
Carter, DJ., Brown J., & Saunders C. (2018).  The Patient's Voice: Australian Health Care Quality and Safety Regulation from the Perspective of the Public. Journal of Law and Medicine. 25(2), 21.
Cespedes, M. Ines, McGree J. M., Drovandi C. C., Mengersen KL., Doecke J. D., & Fripp J. (2018).  AGE DEPENDENT BAYESIAN NETWORKS REVEAL SPATIO-TEMPORAL PATTERNS OF NEURODEGENERATION IN HEALTHY AGEING AND ALZHEIMER’S DISEASE. Alzheimer's & Dementia. 14(7), P1228. doi: 10.1016/j.jalz.2018.06.1727
Chattopadhyay, A., Blaszczyszyn B., & Keeler H. (2018).  Gibbsian On-Line Distributed Content Caching Strategy for Cellular Networks. IEEE Transactions on Wireless Communications. 17(2), 969-981. doi: 10.1109/TWC.2017.2772911
Chen, S., Liu F., Turner I., & Hu X. (2018).  Numerical inversion of the fractional derivative index and surface thermal flux for an anomalous heat conduction model in a multi-layer medium. Applied Mathematical Modelling. 59, 514 - 526. doi: 10.1016/j.apm.2018.01.045
Chen, Z., De Gier J., Hiki I., & Sasamoto T. (2018).  Exact Confirmation of 1D Nonlinear Fluctuating Hydrodynamics for a Two-Species Exclusion Process. Physical Review Letters. 120(24), 240601. doi: 10.1103/PhysRevLett.120.240601
Chen, S.., Liu F., Turner I., & Anh V.. (2018).  A fast numerical method for two-dimensional Riesz space fractional diffusion equations on a convex bounded region. Applied Numerical Mathematics. 134, 66 - 80. doi: 10.1016/j.apnum.2018.07.007
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Doosti, H., & Hall P. (2016).  Making a non-parametric density estimator more attractive, and more accurate, by data perturbation. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 78(2), 445-462. doi: 10.1111/rssb.12120
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Fitzpatrick, B., Lamb D. W., & Mengersen KL. (2016).  Ultrahigh dimensional variable selection for interpolation of point referenced spatial data: A digital soil mapping case study. PLOS ONE. 11(9),  doi: 10.1371/journal.pone.0162489
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Hsieh, J. C. - F., Cramb S., McGree J., Dunn N. A. M., Baade P. D., & Mengersen KL. (2016).  Spatially varying coefficient inequalities: Evaluating how the impact of patient characteristics on breast cancer survival varies by location. PLOS ONE. 11(5),  doi: 10.1371/journal.pone.0155086
Huang, X., Clements A. C. A., Williams G., Mengersen KL., Tong S., & Hu W. (2016).  Bayesian estimation of the dynamics of pandemic (H1N1) 2009 influenza transmission in Queensland: A space–time SIR-based model. Environmental Research. 146, 308–314. doi: 10.1016/j.envres.2016.01.013
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Jiang, J., & Xie H. (2016).  Denoising nonlinear time series using singular spectrum analysis and fuzzy entropy. Chinese Physics Letters. 33(10),  doi: 10.1088/0256-307X/33/10/100501
Johansen, T. A., Perez T., & Cristofaro A. (2016).  Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment. IEEE Transactions on Intelligent Transportation Systems. 17(12), 3407-3422. doi: 10.1109/TITS.2016.2551780
Kang, S. Y., Cramb S., White N., Ball S. J., & Mengersen KL. (2016).  Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data. Geospatial Health. 11(2),  doi: 10.4081/gh.2016.428
Kang, S. Y., McGree J., Drovandi C. C., M. Caley J., & Mengersen KL. (2016).  Bayesian adaptive design: Improving the effectiveness of monitoring of the Great Barrier Reef. Ecological Applications. 26(8), 2637-2648. doi: 10.1002/eap.1409
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Hu, W., Zhang W., Huang X., Clements A., Mengersen KL., & Tong S. (2015).  Weather variability and influenza A (H7N9) transmission in Shanghai, China: A Bayesian spatial analysis. Environmental Research. 136, 405-412. doi: 10.1016/j.envres.2014.07.033
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Kang, S. Yun, McGree J., Baade P. D., & Mengersen KL. (2015).  A Case Study for Modelling Cancer Incidence Using Bayesian Spatio-Temporal Models. Australian & New Zealand Journal of Statistics. 57(3), 325-345. doi: 10.1111/anzs.12127
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Melville, G. J., Welsh A. H., & Stone C. (2015).  Improving the Efficiency and Precision of Tree Counts in Pine Plantations Using Airborne LiDAR Data and Flexible-Radius Plots: Model-Based and Design-Based Approaches. Journal of Agricultural, Biological, and Environmental Statistics. 20(2), 229-257. doi: 10.1007/s13253-015-0205-6
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