Pipeline
Maestrini, L., Bernardi, M. and Livieri, G. Variable selection for heteroskedastic regression models via variational approximations
Maestrini, L., Scealy, J. L., Hui, F. K. C. and Wood, A. T. A. Multiplicative semiparametric regression for manifold-valued responses
Dang, K.-D., Maestrini, L. and Hui, F. K. C. Variational Bayes for mixture of Gaussian structural equation models [manuscript]
Lin, M., Maestrini, L., Livieri, G. and Bernardi, M. Mixture of experts Bayesian learning of deep neural networks
Nolin, F., Maestrini, L., Collins, S. and Ueland, M. Investigation of lipid profiles extracted from soil exposed to decomposing human and pig remains
Pham, H. T., Maestrini, L. and Monica Chiogna. Uncovering structures in graphical models for count data via semiparametric mean field variational Bayes
Thandrayen, T., Maestrini, L., Riley, T., Lovett, R., Draper, G., Dillon, Y., Freebairn, L. Quantifying the missingness of Indigenous status from two administrative sources in regional Australia using capture-recapture methods
Refereed Journal Articles
Maestrini, L., Hui, F. K. C. and Welsh, A. H. (2025+). Restricted maximum likelihood estimation in generalized linear mixed models. To appear in Statistical Science [manuscript (with supplement)] [code]
Hui, F. K. C., Dang, K.-D. and Maestrini, L. (2025). Simultaneous coefficient clustering and sparsity for multivariate mixed models. Journal of Computational and Graphical Statistics, 34 (2), 618–629 [article] [supplement] [code]
Maestrini, L., Aykroyd, R. G. and Wand, M. P. (2025). A variational inference framework for inverse problems. Computational Statistics and Data Analysis, 202, 1–15 [article] [supplement] [code]
Maestrini, L., Bhaskaran, A. and Wand, M. P. (2024). Second term improvement to generalised linear mixed model asymptotics. Biometrika, 111 (3), 1077–1084 [article] [supplement] [code]
Hui, F. K. C., Maestrini, L. and Welsh, A. H. (2024). Homogeneity pursuit and variable selection in regression models for multivariate abundance data. Biometrics, 80 (1), 1–11 [article] [supplement] [code]
Xuan, H., Maestrini, L., Chen, F. and Grazian, C. (2024). Stochastic variational inference for GARCH models. Statistics and Computing, 34 (1), 1–26 [article]
Collins, S., Maestrini, L., Hui F. K. C., Stuart, B. and Ueland, M. (2023). The use of generalised linear mixed models to investigate lipids in textiles as biomarkers of decomposition. iScience, 26 (8), 1–11 [article] [supplement]
Degani, E., Maestrini, L., Toczydłowska, D. and Wand, M. P. (2022). Sparse linear mixed model selection via streamlined variational Bayes. Electronic Journal of Statistics, 16 (2), 5182–5225 [article] [supplement] [code]
Dang, K.-D. and Maestrini, L. (2022). Fitting structural equation models via variational approximations. Structural Equation Modeling: A Multidisciplinary Journal, 29 (6), 839–853 [article] [code]
Collins, S., Maestrini, L., Ueland, M. and Stuart, B. (2022). A preliminary investigation to determine the suitability of pigs as human analogues for post-mortem lipid analysis. Talanta Open, 5, 1–11 [article] [supplement]
Ueland, M., Collins, S., Maestrini, L., Forbes, S. L. and Luong, S. (2021). Fresh vs. frozen human decomposition - a preliminary investigation of lipid degradation products as biomarkers of post-mortem interval. Forensic Chemistry, 24, 1–9 [article] [supplement]
Maestrini, L. and Wand, M. P. (2021). The Inverse G-Wishart distribution and variational message passing. Australian and New Zealand Journal of Statistics, 63 (3), 517–541 [article] [supplement] [code]
Maestrini, L. and Wand, M. P. (2018). Variational message passing for Skew t regression. Stat, 7 (1), 1–11 [article] [supplement]
Refereed Conference Proceeding Articles
Degani, E., Maestrini, L. and Bernardi, M. (2021). Model fitting and Bayesian inference via power expectation propagation. Book of Short Papers SIS 2021, editors Perna, C., Salvati, N. and Schirripa Spagnolo, F., pp. 1026–1031. Pearson, ISBN 9788891927361 [article]
Maestrini, L. and Wand, M. P. (2018). Variational approximations for frequentist and Bayesian inference. Book of Short Papers SIS 2018, editors Abbruzzo, A., Piacentino, D., Chiodi, M. and Brentari, E., pp. 1447–1452. Pearson, ISBN 9788891910233 [article]
Maestrini, L. and Wand, M. P. (2018). Variational message passing for Skew t regression. Proceedings of the 33rd International Workshop on Statistical Modelling, Bristol, pp. 204–208 [article]
PhD Thesis
On Variational Approximations for Frequentist and Bayesian Inference [thesis]