Statistical Modeling and Inference
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2027Mourahib, A., Kiriliouk, A., & Segers, J. (2027). A penalized least-squares estimator for extreme-value models with multiple extreme directions. Computational Statistics & Data Analysis, 225(2), 108460. https://doi.org/10.1016/j.csda.2026.108460 (Original work published 2027)
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2026Léonard, L., Pircalabelu, E., & von Sachs, R. (2026). Inference for High-Dimensional Model Averaging Estimators. Statistica Sinica, 38(2), ... https://doi.org/10.5705/ss.202025.0211 (Original work published 2028)
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Lin, M.-B., Wang, B., Bocart, F. Y. R. P., Hafner, C., & Härdle, W. K. (2026). DAI digital art index: a robust price index for heterogeneous digital assets. Journal of the Royal Statistical Society. Series A, Statistics in society. Accepted/in-press. https://doi.org/10.1093/jrsssa/qnag008 (Original work published 2026)
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Bauwens, L., Dzuverovic, E., & Hafner, C. (2026). Asymmetric models for realized covariances. International Journal of Forecasting, 42(2), 640-656. https://doi.org/10.1016/j.ijforecast.2025.09.005 (Original work published 2026)
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2025Allen, S., Koh, J., Segers, J., & Ziegel, J. (2025). Tail calibration of probabilistic forecasts. Journal of the American Statistical Association, 120(552), 2796-2808. https://doi.org/10.1080/01621459.2025.2506194 (Original work published 2025)
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Van Keilegom, I., & Deketelaere, B. (2025). Quantile regression for interval censored data using an Enriched Laplace distribution. Electronic Journal of Statistics, 19(1), 54-86. https://doi.org/10.1214/24-EJS2334 (Original work published 2025)
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Marion, R., Lederer, J., Govaerts, B., & von Sachs, R. (2025). VC-PCR: A prediction method based on variable selection and clustering. Statistica Neerlandica, 79(1), e12358. https://doi.org/10.1111/stan.12358 (Original work published 2025)
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2024Asenova, S., & Segers, J. (2024). Max-linear graphical models with heavy-tailed factors on trees of transitive tournaments. Advances in Applied Probability, 56(2), 621-665. https://doi.org/10.1017/apr.2023.46 (Original work published 2024)
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Rademacher, D., Krebs, J., & von Sachs, R. (2024). Statistical inference for wavelet curve estimators of symmetric positive definite matrices. Journal of Statistical Planning and Inference, 231, 106140. https://doi.org/10.1016/j.jspi.2023.106140 (Original work published 2024)
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Daraio, C., & Simar, L. (2024). Approximations and inference for envelopment estimators of production frontiers. Journal of Productivity Analysis, 62(2), 197-215. https://doi.org/10.1007/s11123-024-00726-2 (Original work published 2024)
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2023Plassier, V., Portier, F., & Segers, J. (2023). Risk bounds when learning infinitely many response functions by ordinary linear regression. Annales de l’Institut Henri Poincare. B, Probability and Statistics, 59(1), 53-78. https://doi.org/10.1214/22-AIHP1259 (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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Hafner, C., & Herwartz, H. (2023). Asymmetric volatility impulse response functions. Economics Letters, 222, 110968. https://doi.org/10.1016/j.econlet.2022.110968 (Original work published 2023)
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Lambert, P., & Gressani, O. (2023). Penalty parameter selection and asymmetry corrections to Laplace approximations in Bayesian P-splines models. Statistical Modelling : an international journal, 23(5-6), 409-423. https://doi.org/10.1177/1471082X231181173 (Original work published 2023)
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Hafner, C., & Herwartz, H. (2023). Correlation impulse response functions. Finance Research Letters, 57, 104176. https://doi.org/10.1016/j.frl.2023.104176 (Original work published 2023)
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2022Lhaut, S., Sabourin, A., & Segers, J. (2022). Uniform concentration bounds for frequencies of rare events. Statistics & Probability Letters, 189, 109610. https://doi.org/10.1016/j.spl.2022.109610 (Original work published 2022)
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2026
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Li, M., von Sachs, R., & Pircalabelu, E. (2026). Learning shared and individual structure in dynamic networks with degree heterogeneity (LIDAM Discussion Paper ISBA 2026/12).
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2025
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Fève, F., Florens, J.-P., & Simar, L. (2025). Reconciling Engineers and Economists: the Case of a Cost Function for the Distribution of Gas (LIDAM Discussion Paper ISBA 2025/13).
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Bücher, A., Segers, J., & Staud, T. (2025). Consistency of M-estimators for non-identically distributed data: the case of fixed-design distributional regression (LIDAM Discussion Paper ISBA 2025/21).
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2024
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2023
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2022
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2021
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2023Leluc, R., Portier, F., Segers, J., & Zhuman, A. (2023). A Quadrature Rule combining Control Variates and Adaptive Importance Sampling. In Ed. by S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho and A. Oh (ed.), Advances in Neural Information Processing Systems 35 (36th Conference on Neural Information Processing Systems - NeurIPS 2022) (p. p. 11842-11853). NeurIPS. https://doi.org/10.52202/068431-0860
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O’Loughlin, C., Simar, L., & Wilson, P. W. (2023). Methodologies for assessing government efficiency. In António Afonso, João Tovar Jalles and Ana Venâncio (eds) (ed.), Handbook on Public Sector Efficiency (p. p. 72-101 (chap. 4)). E. Elgar. https://doi.org/10.4337/9781839109164.00010
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