Postdoctoral researcher in multivariate extreme value theory and copula modelling. PhD in Statistics from Lancaster University. Knowledge of Machine Learning and Spatial Statistics. Codes primarily in R and Julia.
EDUCATION
BSc in Applied Mathematics to Economics and Management, Lisbon School of Economics and Management, University of Lisbon
MSc in Statistics and Operational Research, Faculty of Sciences, University of Lisbon
MRes in Statistics and Operational Research, Lancaster University
PhD in Statistics, Lancaster University
RESEARCH, TEACHING, or OTHER INTERESTS
Statistics and Probability, Environmental Science, Control and Optimization, Software
5
Scopus Publications
35
Scholar Citations
4
Scholar h-index
1
Scholar i10-index
Scopus Publications
Extreme value methods for estimating rare events in Utopia: EVA (2023) conference data challenge: team Lancopula Utopiversity Lídia Maria André, Ryan Campbell, Eleanor D’Arcy, Aiden Farrell, Dáire Healy, et al. Extremes, 2025 To capture the extremal behaviour of complex environmental phenomena in practice, flexible techniques for modelling tail behaviour are required. In this paper, we introduce a variety of such methods, which were used by the Lancopula Utopiversity team to tackle the EVA (2023) Conference Data Challenge. This data challenge was split into four challenges, labelled C1-C4. Challenges C1 and C2 comprise univariate problems, where the goal is to estimate extreme quantiles for a non-stationary time series exhibiting several complex features. For these, we propose a flexible modelling technique, based on generalised additive models, with diagnostics indicating generally good performance for the observed data. Challenges C3 and C4 concern multivariate problems where the focus is on estimating joint probabilities. For challenge C3, we propose an extension of available models in the multivariate literature and use this framework to estimate joint probabilities in the presence of non-stationary dependence. Finally, for challenge C4, which concerns a 50-dimensional random vector, we employ a clustering technique to achieve dimension reduction and use a conditional modelling approach to estimate extremal probabilities across independent groups of variables.
Neural Bayes estimation and selection of complex bivariate extremal dependence models L. M. André, J. L. Wadsworth, R. Huser Extremes, 2025 Likelihood-free approaches are appealing for performing inference on complex dependence models, either because it is not possible to formulate a likelihood function, or its evaluation is very computationally costly. This is the case for several models available in the multivariate extremes literature, particularly for the most flexible tail models, including those that interpolate between the two key dependence classes of ‘asymptotic dependence’ and ‘asymptotic independence’. We focus on approaches that leverage neural networks to approximate Bayes estimators. In particular, we explore the properties of neural Bayes estimators for parameter inference for several flexible but computationally expensive models to fit, with a view to aiding their routine implementation. Owing to the absence of likelihood evaluation in the inference procedure, classical information criteria such as the Bayesian information criterion cannot be used to select the most appropriate model. Instead, we propose using neural networks as neural Bayes classifiers for model selection. Our goal is to provide a toolbox for simple, fast fitting and comparison of complex extreme-value dependence models, where the best model is selected for a given data set and its parameters subsequently estimated using neural Bayes estimation. We apply our classifiers and estimators to analyse the pairwise extremal behaviour of changes in horizontal geomagnetic field fluctuations at three different locations.
Spatial Modelling of Black Scabbardfish Fishery Off the Portuguese Coast Lídia Maria André, Ivone Figueiredo, M. Lucília Carvalho, Paula Simões, Isabel Natário Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020
RECENT SCHOLAR PUBLICATIONS
Neural Bayes estimation and selection of complex bivariate extremal dependence models LM André, JL Wadsworth, R Huser Extremes, 1-40 , 2025 2025 Citations: 7
Gaussian mixture copulas for flexible dependence modelling in the body and tails of joint distributions LM André, JA Tawn arXiv preprint arXiv:2503.06255 , 2025 2025 Citations: 2
Extreme value methods for estimating rare events in Utopia: EVA (2023) conference data challenge: team Lancopula Utopiversity LM André, R Campbell, E D’Arcy, A Farrell, D Healy, L Kakampakou, ... Extremes 28 (1), 23-45 , 2025 2025 Citations: 2
Return Curves Estimation L André, C Murphy-Barltrop, J Wadsworth 2025
Modelling and inference for the body and tail regions of multivariate data LMBCM André Lancaster University , 2025 2025
Joint modelling of the body and tail of bivariate data LM André, JL Wadsworth, A O'Hagan Computational Statistics & Data Analysis 189, 107841 , 2024 2024 Citations: 10
Modelling dependence between observed and simulated wind speed data using copulas LM André, P de Zea Bermudez Stochastic Environmental Research and Risk Assessment 34 (11), 1725-1753 , 2020 2020 Citations: 7
Spatial modelling of black scabbardfish fishery off the portuguese coast LM André, I Figueiredo, ML Carvalho, P Simões, I Natário International Conference on Computational Science and Its Applications, 332-344 , 2020 2020 Citations: 6
Copula models for dependence: comparing classical and bayesian approaches LMBCM André PQDT-Global , 2019 2019 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Joint modelling of the body and tail of bivariate data LM André, JL Wadsworth, A O'Hagan Computational Statistics & Data Analysis 189, 107841 , 2024 2024 Citations: 10
Neural Bayes estimation and selection of complex bivariate extremal dependence models LM André, JL Wadsworth, R Huser Extremes, 1-40 , 2025 2025 Citations: 7
Modelling dependence between observed and simulated wind speed data using copulas LM André, P de Zea Bermudez Stochastic Environmental Research and Risk Assessment 34 (11), 1725-1753 , 2020 2020 Citations: 7
Spatial modelling of black scabbardfish fishery off the portuguese coast LM André, I Figueiredo, ML Carvalho, P Simões, I Natário International Conference on Computational Science and Its Applications, 332-344 , 2020 2020 Citations: 6
Gaussian mixture copulas for flexible dependence modelling in the body and tails of joint distributions LM André, JA Tawn arXiv preprint arXiv:2503.06255 , 2025 2025 Citations: 2
Extreme value methods for estimating rare events in Utopia: EVA (2023) conference data challenge: team Lancopula Utopiversity LM André, R Campbell, E D’Arcy, A Farrell, D Healy, L Kakampakou, ... Extremes 28 (1), 23-45 , 2025 2025 Citations: 2
Copula models for dependence: comparing classical and bayesian approaches LMBCM André PQDT-Global , 2019 2019 Citations: 1
Return Curves Estimation L André, C Murphy-Barltrop, J Wadsworth 2025
Modelling and inference for the body and tail regions of multivariate data LMBCM André Lancaster University , 2025 2025
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
R package "ReturnCurves"
Industry, Institute, or Organisation Collaboration
