High Dimensional Data and Data Sciences
lidam | Louvain-la-Neuve, Mons
You will find below our recent publications in high dimensional data and data sciences.
LIDAM Recent Publications in High Dimensional Data and Data Sciences
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2026Lederer, J., & von Sachs, R. (2026). Simultaneous estimation of stable parameters for multiple autoregressive processes from datasets of nonuniform sizes. Journal of Time Series Analysis, 47(2), 345-363. (Original work published 2026)
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Pircalabelu, E., & Bing, X. (2026). Overlapping clustering of time dependent variables for fMRI data. Journal of the Royal Statistical Society. Series C, Applied statistics. Submitted. (Original work published 2026)
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2024Jacquemain, A., Heuchenne, C., & Pircalabelu, E. (2024). A penalised bootstrap estimation procedure for the explained Gini coefficient. Electronic Journal of Statistics, 18(1), 247-300. https://doi.org/10.1214/23-EJS2200 (Original work published 2024)
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Nezakati Rezazadeh, E., & Pircalabelu, E. (2024). Estimation and inference in sparse multivariate regression and conditional Gaussian graphical models under an unbalanced distributed setting. Electronic Journal of Statistics, 18(1), 599-652. https://doi.org/10.1214/23-EJS2193 (Original work published 2024)
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2023Nezakati Rezazadeh, E., & Pircalabelu, E. (2023). Unbalanced distributed estimation and inference for the precision matrix in Gaussian graphical models. Statistics and Computing, 33, 47. https://doi.org/10.1007/s11222-023-10211-9 (Original work published 2023)
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Pircalabelu, E., & Claeskens, G. (2023). Linear manifold modeling and graph estimation based on multivariate functional data with different coarseness scales. Journal of Computational and Graphical Statistics, 32(2), 378-387. https://doi.org/10.1080/10618600.2022.2108818 (Original work published 2023)
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