Statistical Modelling and Inference
lidam | Louvain-la-Neuve, Mons
You will find below our recent publications in statistical modelling and inference.
LIDAM Recent Publications in Statistical Modelling 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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Simar, L., & Wilson, P. W. (2026). A Fast Method for Implementing Hypothesis Tests with Multiple Sample Splits in Nonparametric Models of Production. Computational Economics, 67(5), 3777-3813. https://doi.org/10.1007/s10614-025-10995-0 (Original work published 2026)
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Cazals, C., Florens, J.-P., & Simar, L. (2026). Single Index Models for nonparametric conditional frontiers. Econometrics and Statistics. Accepted/in-press. https://doi.org/10.1016/j.ecosta.2026.04.001 (Original work published 2026)
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Mastromarco, C., & Simar, L. (2026). Nonparametric spatial frontier models for productivity analysis: evidence from EU regions. Journal of Productivity Analysis. Accepted/in-press. https://doi.org/10.1007/s11123-026-00796-4 (Original work published 2026)
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Kneip, A., Simar, L., & Wilson, P. W. (2026). Conical FDH estimators for testing returns to scale and making inference about changes in productivity. Econometric Reviews, 45(4), 482-517. https://doi.org/10.1080/07474938.2025.2584132 (Original work published 2026)
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Lhaut, S., Rootzén, H., & Segers, J. (2026). Simulation of multivariate extremes: A Wasserstein–Aitchison GAN approach. Extremes. Accepted/in-press. https://doi.org/10.1007/s10687-026-00530-1 (Original work published 2026)
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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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Jacquemain, A., & Heuchenne, C. (2026). LorenzRegression: An Implementation of the Lorenz and Penalized Lorenz Regressions in R. Journal of Statistical Software, 117(6), 1-33. https://doi.org/10.18637/jss.v117.i06 (Original work published 2026)
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Teng, H.-W., Härdle, W. K., Osterrieder, J., Hafner, C., & et al. (2026). Digital assets: risks, regulations, mitigation. Financial Innovation, 12, 65. https://doi.org/10.1186/s40854-025-00848-y (Original work published 2026)
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Bailly, G., & von Sachs, R. (2026). Nonlinear wavelet threshold estimation of time-varying covariance matrices in a log-Euclidean manifold. Journal of Time Series Analysis, 47(5), 1044-1065. https://doi.org/10.1111/jtsa.70011 (Original work published 2026)
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2025Hafner, C., Herwartz, H., & Wang, S. (2025). Statistical Identification of Independent Shocks with Kernel-based Maximum Likelihood Estimation and an Application to the Global Crude Oil Market. Journal of Business and Economic Statistics, 43(2), 423-438. https://doi.org/10.1080/07350015.2024.2388657 (Original work published 2025)
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Delhelle, M., & Van Keilegom, I. (2025). Copula based dependent censoring in cure models. Test, 34(2), 361-382. https://doi.org/10.1007/s11749-024-00961-7 (Original work published 2025)
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Leluc, R., Portier, F., Segers, J., & Zhuman, A. (2025). Speeding up Monte Carlo Integration: Control Neighbors for Optimal Convergence. Bernoulli : a journal of mathematical statistics and probability, 31(2), 1160-1180. https://doi.org/10.3150/24-BEJ1765 (Original work published 2025)
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Allen, 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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Daraio, C., Di Leo, S., & Simar, L. (2025). Impact of a Regulatory Target and External Factors on the Waste Efficiency of Italian Municipalities. Waste Management and Research, 43(4), 580-592. https://doi.org/10.1177/0734242X241262698 (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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Mourahib, A., Kiriliouk, A., & Segers, J. (2025). Multivariate generalized Pareto distributions along extreme directions. Extremes, 28(2), 239-272. https://doi.org/10.1007/s10687-024-00501-4 (Original work published 2025)
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Hafner, C. (2025). Explanatory factors of French retail wine prices. Applied Economics Letters, 32(2), 259-262. https://doi.org/10.1080/13504851.2023.2266565 (Original work published 2025)
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Hafner, C., Linton, O. B., & Wang, L. (2025). The Permanent and Temporary Effects of Stock Splits on Liquidity in a Dynamic Semiparametric Model. Journal of Business and Economic Statistics. Accepted/in-press. https://doi.org/10.1080/07350015.2025.2551246 (Original work published 2025)
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Daraio, C., Di Leo, S., & Simar, L. (2025). Conical Free Disposal Hull estimators of directional distances and Luenberger productivity indices for general technologies. European Journal of Operational Research, 323(3), 907-917. https://doi.org/10.1016/j.ejor.2024.12.025 (Original work published 2025)
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Kiriliouk, A., Lee, J., & Segers, J. (2025). X-Vine Models for Multivariate Extremes. Journal of the Royal Statistical Society. Series B, Statistical methodology, 87(3), 579-602. https://doi.org/10.1093/jrsssb/qkae105 (Original work published 2025)
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Mastromarco, C., Simar, L., & Van Keilegom, I. (2025). Estimating Nonparametric Conditional Frontiers and Efficiencies: A New Approach. The Econometrics Journal, 28(3), 502-528. https://doi.org/10.1093/ectj/utae025 (Original work published 2025)
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Simar, L., Zelenyuk, V., & Zhao, S. (2025). Statistical Inference for Hicks–Moorsteen Productivity Indices. Annals of Operations Research, 351, 1675-1703. https://doi.org/10.1007/s10479-024-06288-8 (Original work published 2025)
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Piulachs, X., El Ghouch, A., & Van Keilegom, I. (2025). Testing for the Functional Form of a Continuous Covariate in the Shared-Parameter Joint Model. Statistics in Medicine, 44(5), e10340. https://doi.org/10.1002/sim.10340 (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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2024Daraio, C., Di Leo, S., & Simar, L. (2024). Viable eco‐efficiency targets for waste collection communities. Scientific Reports, 14, 15038. https://doi.org/10.1038/s41598-024-66077-y (Original work published 2024)
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Hu, S., Peng, Z., & Segers, J. (2024). Modeling multivariate extreme value distributions via Markov trees. Scandinavian Journal of Statistics : theory and applications, 51(2), 760-800. https://doi.org/10.1111/sjos.12698 (Original work published 2024)
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Hafner, C., Linton, O. B., & Wang, L. (2024). Dynamic Autoregressive Liquidity (DArLiQ). Journal of Business and Economic Statistics, 42(2), 774-785. https://doi.org/10.1080/07350015.2023.2238790 (Original work published 2024)
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Ortega Jiménez, P., Pellerey, F., Sordo, M., & Suárez-Llorens, A. (2024). Probability equivalent level for CoVaR and VaR. Insurance: Mathematics and Economics, 115, 22-35. https://doi.org/10.1016/j.insmatheco.2023.12.004 (Original work published 2024)
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Parmeter, C. F., Simar, L., Van Keilegom, I., & Zelenyuk, V. (2024). Inference in the nonparametric stochastic frontier model. Econometric Reviews, 43(7), 518-539. https://doi.org/10.1080/07474938.2024.2339193 (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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Asenova, 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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Hentschel, M., Engelke, S., & Segers, J. (2024). Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions. Journal of the American Statistical Association. Published. https://doi.org/10.1080/01621459.2024.2371978 (Original work published 2024)
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Janssen, A., & Segers, J. (2024). Invariance properties of limiting point processes and applications to clusters of extremes. Dependence Modeling, 12(1), 20230109. https://doi.org/10.1515/demo-2023-0109 (Original work published 2024)
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Pham, M., Simar, L., & Zelenyuk, V. (2024). Statistical Inference for Aggregation of Malmquist Productivity Indices. Operations research, 72(4), 1615-1629. https://doi.org/10.1287/opre.2022.2424 (Original work published 2024)
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Simar, L., Zelenyuk, V., & Zhao, S. (2024). Inference for aggregate efficiency: Theory and guidelines for practitioners. European Journal of Operational Research, 316(1), 240-254. https://doi.org/10.1016/j.ejor.2024.01.028 (Original work published 2024)
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Hohage, T., Maréchal, P., Simar, L., & Vanhems, A. (2024). A mollifier approach to the deconvolution of probability densities. Econometric Theory, 40(2), 320-359. https://doi.org/10.1017/S0266466622000457 (Original work published 2024)
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D’Adamo, I., Daraio, C., Di Leo, S., & Simar, L. (2024). A Flexible and Sustainable Analysis of Waste Efficiency at the European Level. Global Journal of Flexible Systems Management, 25(4), 881-894. https://doi.org/10.1007/s40171-024-00416-w (Original work published 2024)
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Fülle, M. J., Hafner, C., Herwartz, H., & Lange, A. (2024). BEKKs: An R Package for Estimation of Conditional Volatility of Multivariate Time Series. Journal of Statistical Software, 111(4), 1-34. https://doi.org/10.18637/jss.v111.i04 (Original work published 2024)
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2023Fève, F., Florens, J.-P., & Simar, L. (2023). Proportional incremental cost probability functions and their frontiers. Empirical Economics, 64(6), 2721-2756. https://doi.org/10.1007/s00181-023-02386-x (Original work published 2023)
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Simar, L., & Wilson, P. (2023). Another Look at Productivity Growth in Industrialized Countries. Journal of Productivity Analysis, 60(3), 257-272. https://doi.org/10.1007/s11123-023-00689-w (Original work published 2023)
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Fall, F. S., Tchakoute Tchuigoua, H., Vanhems, A., & Simar, L. (2023). Investigating the unobserved heterogeneity effect on outreach to women: lessons from microfinance institutions. Annals of Operations Research, 328(2), 1365-1386. https://doi.org/10.1007/s10479-023-05353-y (Original work published 2023)
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Simar, L., Zelenyuk, V., & Zhao, S. (2023). Further Improvements of Finite Sample Approximation of Central Limit Theorems for Envelopment Estimators. Journal of Productivity Analysis, 59(2), 189-194. https://doi.org/10.1007/s11123-023-00661-8 (Original work published 2023)
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Plassier, 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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Clémençon, S., Jalalzai, H., Lhaut, S., Sabourin, A., & Segers, J. (2023). Concentration bounds for the empirical angular measure with statistical learning applications. Bernoulli : a journal of mathematical statistics and probability, 29(4), 2797-2827. https://doi.org/10.3150/22-BEJ1562 (Original work published 2023)
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Simar, L., & Wilson, P. W. (2023). Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs. Journal of Business and Economic Statistics, 41(4), 1391-1403. https://doi.org/10.1080/07350015.2022.2110882 (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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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). 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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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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Lambert, P. (2023). Nonparametric density estimation and risk quantification from tabulated sample moments. Insurance: Mathematics and Economics, 108, 177-189. https://doi.org/10.1016/j.insmatheco.2022.12.004 (Original work published 2023)
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Bocart, F. Y. R. P., Hafner, C., Kasperskaya, Y., & Sagarra, M. (2023). Investing in superheroes? Comic art as a new alternative investment. The Journal of Alternative Investments, 25(3), 9-27. https://doi.org/10.3905/jai.2022.1.174 (Original work published 2023)
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Oorschot, J., Segers, J., & Zhou, C. (2023). Tail inference using extreme U-statistics. Electronic Journal of Statistics, 17(1), 1113-1159. https://doi.org/10.1214/23-EJS2129 (Original work published 2023)
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Lambert, P. (2023). Comments on: Nonparametric estimation in mixture cure models with covariates. Test, 32(2), 506-509. https://doi.org/10.1007/s11749-023-00860-3 (Original work published 2023)
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Kreyenfeld, M., Konietzka, D., Lambert, P., & Ramos, V. J. (2023). Second Birth Fertility in Germany: Social Class, Gender, and the Role of Economic Uncertainty. European Journal of Population, 39(5). https://doi.org/10.1007/s10680-023-09656-5 (Original work published 2023)
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Asenova, S., & Segers, J. (2023). Extremes of Markov random fields on block graphs: max-stable limits and structured Hüsler–Reiss distributions. Extremes, 26(3), 433-468. https://doi.org/10.1007/s10687-023-00467-9 (Original work published 2023)
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2022Hafner, C., & Majeri, S. (2022). Analysis of cryptocurrency connectedness based on network to transaction volume ratios. Digital Finance, 4(2-3), 187-216. https://doi.org/10.1007/s42521-022-00054-w (Original work published 2022)
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Lhaut, 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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Kyriakopoulou, D., & Hafner, C. (2022). Reconciling negative return skewness with positive time-varying risk premia. Econometric Reviews, 41(8), 877-894. https://doi.org/10.1080/07474938.2022.2072323 (Original work published 2022)
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Nguyen, B. H., Simar, L., & Zelenyuk, V. (2022). Data sharpening for improving central limit theorem approximations for data envelopment analysis-type efficiency estimators. European Journal of Operational Research, 303(3), 1469-1480. https://doi.org/10.1016/j.ejor.2022.03.038 (Original work published 2022)
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2024Simar, L., & Wilson, P. W. (2024). Inference in Dynamic, Nonparametric Models of Production for General Technologies. In A. Emrouznejad, e.a. (eds) (ed.), Advances in the Theory and Applications of Performance Measurement and Management (p. p. 9-20). Springer. https://doi.org/10.1007/978-3-031-61597-9
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Lhaut, S., & Segers, J. (2024). An asymptotic expansion of the empirical angular measure for bivariate extremal dependence. In M. Barigozzi, S. Hörmann, D. Paindaveine (eds) (ed.), Recent Advances in Econometrics and Statistics (p. p. 187-208). Springer. https://doi.org/10.1007/978-3-031-61853-6_10
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Simar, L., & Wilson, P. W. (2024). New Tools for Evaluating the Performance of Healthcare Providers Using DEA and FDH Estimators. In ed. by Shawna Grosskopf, Vivian Valdmanis, Valentin Zelenyuk (ed.), The Cambridge Handbook of Healthcare : Productivity, Efficiency, Effectiveness (p. 351-403 (Chap. 12)). Cambridge University Press. https://doi.org/10.1017/9781009483766.013
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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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