Division of Biomedical Data Science

Research topics

Selected publications

Preprints

Cremaschi, A., Cadonna, A., Guglielmi, A., and Quintana, F. (2023). A change-point random partition model for large spatio-temporal datasets. doi:10.48550/arXiv.2312.12396

Cremaschi, A., Wertz, T.M., and De Iorio, M. (2023). Repulsion, chaos and equilibrium in mixture models. doi:10.48550/arXiv.2306.10669

Cremaschi, A., Yang, W., De Iorio, M., Evans, W.E., Yang, J.J., and Rosner, G.L. (2023). Bayesian modelling of response to therapy and drug-sensitivity in acute lymphoblastic leukemia. doi:10.48550/arXiv.2311.04408

Franzolini, B., De Iorio, M., and Eriksson, J. (2023). Conditional partial exchangeability: a probabilistic framework for multi-view clustering. doi:10.48550/arXiv.2307.01152

Harris, A., Cremaschi, A., Lim, T.S., De Iorio, M., and Kwa, C.G. (2023). From past to future: digital methods towards artefact analysis. doi:10.48550/arXiv.2312.13790

van den Boom, W., Cremaschi, A., and Thiery, A. H. (2024). Doubly adaptive importance sampling. doi:10.48550/arXiv.2404.18556

van den Boom, W., De Iorio, M., Beskos, A., and Jasra, A. (2023). Graph sphere: from nodes to supernodes in graphical models. doi:10.48550/arXiv.2310.11741

van den Boom, W., De Iorio, M., Qian, F., and Guglielmi, A. (2023). The Multivariate Bernoulli detector: change point estimation in discrete survival analysis. doi:10.48550/arXiv.2308.10583

Forthcoming

Beraha, M., Guglielmi, A., Quintana, F.A., De Iorio, M., Eriksson, J.G., and Yap, F. (2023). Childhood obesity in Singapore: a Bayesian nonparametric approach. Statistical Modelling, advance online publication. doi:10.1177/1471082X231185892

Natarajan, A., De Iorio, M., Heinecke, A., Mayer, E., Glenn, S. (2023). Cohesion and repulsion in Bayesian distance clustering. Journal of the American Statistical Association, advance online publication. doi:10.1080/01621459.2023.2191821

2024

Cremaschi, A., De Iorio, M., Kothandaraman, N., Yap, F., Tint, M.T., and Eriksson, J. (2024). Joint modeling of association networks and longitudinal biomarkers: an application to childhood obesity. Statistics in Medicine, 43(6), 1135–1152. doi:10.1002/sim.9994

Feng, S.F., van den Boom, W., De Iorio, M., Thng, G.J., Chan, J.K.Y., Chen, H.Y., Tan, K.H., and Kee, M.Z.L. (2024). Joint modelling of mental health markers through pregnancy: a Bayesian semi-parametric approach. Journal of Applied Statistics, 51(2), 388–405. doi:10.1080/02664763.2022.2154329

Franzolini, B., Beskos, A., De Iorio, M., Poklewski Koziell, W., and Grzeszkiewicz, K. (2024). Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the U.S. stock market. The Annals of Applied Statistics, 18(1), 555–584. doi:10.1214/23-AOAS1801

Husain, S.F., Cremaschi, A., Suaini, N.H.A., De Iorio, M., Loo, E.X.L., Shek, L.P., Goh, A.E.N., Meaney, M.J., Tham, E.H., and Law, E.C. (2024). Maternal asthma symptoms during pregnancy on child behaviour and executive function: a Bayesian phenomics approach. Brain, Behavior, and Immunity, 118, 202–209. doi:10.1016/j.bbi.2024.02.028

Molinari, M., Cremaschi, A., De Iorio, M., Chaturvedi, N., Hughes, A.D., and Tillin, T. (2024). Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases. Journal of Applied Statistics, 51(1), 114–138. doi:10.1080/02664763.2022.2116746

Natarajan, A., van den Boom, W., Odang, K.B., and De Iorio, M. (2024). On a wider class of prior distributions for graphical models. Journal of Applied Probability, 61(1), 230–243. doi:10.1017/jpr.2023.33

Qian, F., van den Boom, W., and See, K.C. (2024). The new global definition of acute respiratory distress syndrome: insights from the MIMIC-IV database. Intensive Care Medicine, 50(4), 508–509. doi:10.1007/s00134-024-07383-x

Saini, S., Manai, G., van den Boom, W., De Iorio, M., and Qian, F. (2024). Invoice level forecasting with discrete survival methods for effective forecasting of account receivables in supply chain. Discover Analytics, 2, 5. doi:10.1007/s44257-024-00013-2

2023

Cremaschi, A., Argiento, R., De Iorio, M., Shirong, C., Chong, Y.S., Meaney, M.J., and Kee, M.Z. (2023) Seemingly Unrelated Multi-State processes: a Bayesian semiparametric approach. Bayesian Analysis, 18(3), 753–775. doi:10.1214/22-BA1326

De Iorio, M., Favaro, S., Guglielmi, A., and Ye, L. (2023) Bayesian nonparametric mixture modeling for temporal dynamics of gender stereotypes. The Annals of Applied Statistics, 17(3), 2256–2278. doi:10.1214/22-AOAS1717

Franzolini, B., Cremaschi, A., van den Boom, W., and De Iorio, M. (2023). Bayesian clustering of multiple zero-inflated outcomes. Philosophical Transactions of the Royal Society A, 381(2247), 20220145. doi:10.1098/rsta.2022.0145

Franzolini, B., Lijoi, A., and Prunster, I. (2023) Model selection for maternal hypertensive disorders with symmetric hierarchical Dirichlet processes. The Annals of Applied Statistics, 17(1), 313–332. doi:10.1214/22-AOAS1628

Kee, M.Z.L., Cremaschi, A., De Iorio, M., Chen, H., Montreuil, T., Nguyen, T.V., Côté, S.M., O’Donnell, K.J., Giesbrecht, G.F., Letourneau, N., Chan, C.Y., and Meaney, M.J. (2023). Perinatal trajectories of maternal depressive symptoms in prospective, community-based cohorts across 3 continents. JAMA Network Open, 6(10), e2339942. doi:10.1001/jamanetworkopen.2023.39942

Qian, F., van den Boom, W., and See, K.C. (2023). Real-world evidence challenges controlled hypoxemia guidelines for critically-ill patients with chronic obstructive pulmonary disease. Intensive Care Medicine, 49(9), 1133–1135. doi:10.1007/s00134-023-07166-w

van den Boom, W., De Iorio, M., and Beskos, A. (2023). Bayesian learning of graph substructures. Bayesian Analysis, 18(4), 1311–1339. doi:10.1214/22-BA1338

Yu, X., Nott, D.J., & Smith, M.S. (2023). Variational inference for cutting feedback in misspecified models. Statistical Science, 38(3), 490–509. doi:10.1214/23-STS886

Young, A.L., van den Boom, W., Schroeder, R.A., Krishnamoorthy, V., Raghunathan, K., Wu, H.T., and Dunson, D.B. (2023). Mutual information: measuring nonlinear dependence in longitudinal epidemiological data. PLOS ONE, 18(4), e0284904. doi:10.1371/journal.pone.0284904

2022

Argiento, R., and De Iorio, M. (2022). Is infinity that far? A Bayesian nonparametric perspective of finite mixture models. The Annals of Statistics 50(5), 2641–2663. doi:10.1214/22-AOS2201

Harris, A. (2022) The sīmā boundary markers of Angkor: a critical reappraisal. Artibus Asiae, 82(2), 141-178.

Harris, A., Tina, T., Sreytouch, S., Horth, H., Vouchnea, C., and Somala, C. (2022). Towards a temporal understanding of Angkor Thom’s Theravada “Buddhist Terrace” archaeology. Asian Archaeology, 6, 167-183. doi:10.1007/s41826-022-00056-y

Molinari, M., Cremaschi, A., De Iorio, M., Chaturvedi, N., Hughes, A.D., and Tillin, T. (2022). Bayesian nonparametric modelling of multiple graphs with an application to ethnic metabolic differences. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(5), 1181–1204. doi:10.1111/rssc.12570

Mozdzen, A., Cremaschi, A., Cadonna, A., Guglielmi, A., and Kastner, G. (2022). Bayesian modeling and clustering for spatio-temporal areal data: an application to Italian unemployment. Spatial Statistics, 52, 10715. doi:10.1016/j.spasta.2022.100715

van den Boom, W., Beskos, A., and De Iorio, M. (2022). The G-Wishart weighted proposal algorithm: efficient posterior computation for Gaussian graphical models. Journal of Computational and Graphical Statistics, 31(4), 1215–1224. doi:10.1080/10618600.2022.2050250

van den Boom, W., De Iorio, M., and Tallarita, M. (2022). Bayesian inference on the number of recurrent events: a joint model of recurrence and survival. Statistical Methods in Medical Research, 31(1), 139–153. doi:10.1177/09622802211048059

van den Boom, W., Jasra, A., De Iorio, M., Beskos, A., and Eriksson, J.G. (2022). Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo. Statistics and Computing, 32(3), 36. doi:10.1007/s11222-022-10093-3

2021

Cremaschi, A., De Iorio, M., Chong, Y.S., Meaney, M., and Kee, M. (2021). A Bayesian nonparametric approach to dynamic item-response modelling: an application to the GUSTO cohort study. Statistics in Medicine, 40(27), 6021–6037. doi:10.1002/sim.9167

Molinari, M., de Iorio, M., Chaturvedi, N., Hughes, A. and Tillin, T. (2021). Modelling ethnic differences in the distribution of insulin resistance via Bayesian nonparametric processes: an application to the SABRE cohort study. The International Journal of Biostatistics, 17(1), 153–164. doi:10.1515/ijb-2019-0108