Published peer-reviewed articles
2025
Bladt, M., & Øhlenschlæger, C. (2025). Heterogeneous extremes in the presence of random covariates and censoring. Electronic Journal of Statistics, 19:(1): 2511–2560. doi:10.1214/25-EJS2393, arXiv:2406.06113.
Bladt, M., & Furrer, C. (2025). Conditional Aalen–Johansen estimation. Scandinavian Journal of Statistics, 52(2): 873–902. doi:10.1111/sjos.12774, arXiv:2303.02119.
Beirlant, J., & Bladt, M. (2025). Tail Parameter Estimation using Non-Linear Regression on Model Plots. Extremes, to appear. doi:10.1007/s10687-025-00506-7.
Bladt, M., Minca, A., & Peralta, O. (2025). Approximations of semi-Markov processes and insurance policy valuation. Finance and Stochastics, to appear. arXiv:2312.06784.
Bladt, M., & Pittarello, G. (2025). Individual claims reserving using the Aalen–Johansen estimator. ASTIN Bulletin: The journal of the IAA, 55: 29–49. doi:10.1017/asb.2024.28, arXiv:2311.07384.
2024
Albrecher, H., & Bladt, M. (2024). Informed censoring: the parametric combination of data and expert information. Journal of Statistical Planning and Inference, 233: 106171. doi:10.1016/j.jspi.2024.106171, arXiv:2206.13091.
Bladt, M., & Peralta, O. (2024). Strongly convergent homogeneous approximations to inhomogeneous Markov jump processes and applications, 50(1): 334–355. Mathematics of Operations Research. doi:10.1287/moor.2022.0153, arXiv:2204.02954.
Ahmad, J., Bladt, M., & Bladt, M. (2024). Estimating absorption time distributions of general Markov jump processes. Scandinavian Journal of Statistics, 1–30. doi:10.1111/sjos.12679, arXiv:2207.11303.
Bladt, M., & Furrer, C. (2024). Expert Kaplan–Meier estimation. Scandinavian Actuarial Journal, 2024(1): 1–27. doi:10.1080/03461238.2023.2197442, arXiv:2206.13120.
Bladt, M., & Brimnes Gardner, C. (2024). Joint discrete and continuous matrix distribution modelling. Stochastic Models, 40(1): 1–37. doi:10.1080/15326349.2023.2185257, arXiv:2207.01364.
2023
Beirlant, J., Bladt, M., Maribe, G., & Verster, A. (2023). Estimation of tail parameters with missing largest observations. Electronic Journal of Statistics, 17(2): 3728–3761. doi:10.1214/23-EJS2191.
Bladt, M., & Shaiderman, D. (2023). Characterisation of exchangeable sequences through empirical distributions. Electronic Communications in Probability, 28(56): 1–11. doi:10.1214/23-ECP553, arXiv:1903.07861.
Bladt, M., & Yslas, J. (2023). Robust claim frequency modeling through phase-type mixture-of-experts regression. Insurance: Mathematics and Economics, 111: 1–22. doi:10.1016/j.insmatheco.2023.02.008, ssrn:4310567.
Bladt, M., & Yslas, J. (2023). Phase-type mixture-of-experts regression for loss severities. Scandinavian Actuarial Journal, 2023(4): 303–329. doi:10.1080/03461238.2022.2097019, arXiv:2111.00581.
Bladt, M. (2023). A tractable class of Multivariate Phase-type distributions for loss modeling. North American Actuarial Journal, 27(4): 1–21. doi:10.1080/10920277.2023.2167833, arXiv:2110.05179.
Albrecher, H., Bladt, M., & Mueller, A. (2023). Joint lifetime modelling with matrix distributions. Dependence Modeling, 11(1): 1–22. doi:10.1515/demo-2022-0153, arXiv:2207.01279.
Bladt, M., Fuino, M., Shemendyuk, A., & Wagner, J. (2023). Modeling the Burden of Long-Term Care for Institutionalized Elderly Based on Care Duration and Intensity. Annals of Actuarial Science, 17(1): 83–117. doi:10.1017/S1748499522000136.
Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2023). Continuous scaled phase-type distributions. Stochastic Models, 39(2): 1–30. doi:10.1080/15326349.2022.2089683, arXiv:2103.02457.
2022
Albrecher, H., Bladt, M., & Mueller, A. (2022). Penalised likelihood methods for phase-type dimension selection. Statistics & Risk Modeling, 39(3-4): 75–92. doi:10.1515/strm-2021-0026.
Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022). Mortality modeling and regression with matrix distributions. Insurance: Mathematics and Economics, 107: 68–87. doi:10.1016/j.insmatheco.2022.08.001, arXiv:2011.03219.
Bladt, M., Hashorva, E., & Shevchenko, G. (2022). Tail Measures and Regular Variation. Electronic Journal of Probability, 27: 1–43. doi:10.1214/22-EJP788, arXiv:2103.04396.
Bladt, M., & McNeil, A. J. (2022). Time series models with infinite-order partial copula dependence. Dependence Modeling, 10(1): 87–107. doi:10.1515/demo-2022-0105, arXiv:2107.00960.
Bladt, M., & Yslas, J. (2022). Heavy-tailed phase-type distributions: a unified approach. Extremes, 25: 1–37. doi:10.1007/s10687-022-00436-8, arXiv:2107.09023.
Bladt, M. (2022). Phase-type distributions for claim severity regression modeling. ASTIN Bulletin: The journal of the IAA, 52(2): 417–448. doi:10.1017/asb.2021.40, arXiv:2110.05207.
Bladt, M. (2022). Fractional inhomogeneous multi-state models in life insurance. Scandinavian Actuarial Journal, 2022(6): 1–22. doi:10.1080/03461238.2021.1998921, arXiv:2110.05199.
2021
Bladt, M., & McNeil, A. J. (2021). Time series copula models using d-vines and v-transforms. Econometrics and Statistics, 24: 27–48. doi:10.1016/j.ecosta.2021.07.004, arXiv:2006.11088.
Bladt, M., Albrecher, H., & Beirlant, J. (2021). Trimmed extreme value estimators for censored heavy-tailed data. Electronic Journal of Statistics, 15(1): 3112–3136. doi:10.1214/21-EJS1857.
Albrecher, H., Bladt, M., & Vatamidou, E. (2021). Efficient simulation of ruin probabilities when claims are mixtures of heavy and light tails. Methodology and Computing in Applied Probability, 23(4): 1237–1255. doi:10.1007/s11009-020-09799-6.
2020
Albrecher, H., Bladt, M., & Bladt, M. (2020). Multivariate fractional phase-type distributions. Fractional Calculus and Applied Analysis, 23(5): 1431–1451. doi:10.1515/fca-2020-0071, arXiv:2003.11122.
Albrecher, H., Bladt, M., & Bladt, M. (2020). Multivariate matrix Mittag–Leffler distributions. Annals of the Institute of Statistical Mathematics, 1–26. doi:10.1007/s10463-020-00750-7.
Albrecher, H., Bladt, M., & Bladt, M. (2020). Matrix Mittag–Leffler distributions and modeling heavy-tailed risks. Extremes, 23(3): 425–450. doi:10.1007/s10687-020-00377-0.
Bladt, M., Albrecher, H., & Beirlant, J. (2020). Threshold selection and trimming in extremes. Extremes, 23(4): 629–665. doi:10.1007/s10687-020-00385-0.
2019
Bladt, M., Albrecher, H., & Beirlant, J. (2019). Combined tail estimation using censored data and expert information. Scandinavian Actuarial Journal, 6: 503–525. doi:10.1080/03461238.2019.1694974.
Daily-Amir, D., Albrecher, H., Bladt, M., & Wagner, J. (2019). On Market Share Drivers in the Swiss Mandatory Health Insurance Sector. Risks, 7(4): 114. doi:10.3390/risks7040114.
Albrecher, H., Bladt, M., Kortschak, D., Prettenthaler, F., & Swierczynski, T. (2019). Flood occurrence change-point analysis in the paleoflood record from Lake Mondsee (NE Alps). Global and Planetary Change, 178: 65–76. doi:10.1016/j.gloplacha.2019.04.009.
2018
Albrecher, H., Bäuerle, N., & Bladt, M. (2018). Dividends: From refracting to ratcheting. Insurance: Mathematics and Economics, 83: 47–58. doi:10.1016/j.insmatheco.2018.09.003.
Submitted manuscripts
Albrecher, H., Bladt, M., & Mueller, A. Regularized matrix exponential distributions and lapse modeling. The case of the French credit life insurance market. Preprint, University of Copenhagen and University of Lausanne.
Bladt, M., & González Cázares, J. Bayesian non-parametric survival estimation: stochastic hyperparameter sequences and distribution splicing. Preprint, University of Copenhagen, National Autonomous University of Mexico. arXiv:2505.01604.
Beirlant, J., Bladt, M., & Keilegom, I.V. Non-parametric cure models through extreme-value tail estimation. Preprint, KU Leuven, University of Copenhagen. arXiv:2504.00243.
Bladt, M., Glargaard, L., & Henningsen, T. Conditional Extreme Value Estimation for Dependent Time Series. Preprint, University of Copenhagen. arXiv:2503.22366.
Bladt, M., Cheung, E. C. K., Peralta, O., & Woo, J-K. Modeling discrete common-shock risks through matrix distributions. University of Copenhagen, Autonomous Technological Institute of Mexico, University of New South Wales. arXiv:2502.21172.
Bladt, M., Goegebeur, Y., & Guillou, A. Estimation of the conditional tail moment risk measure under random right censoring. Preprint, University of Copenhagen, University of Southern Denmark, Université de Strasbourg. hal-04879997.
Bladt, M., Goegebeur, Y., & Guillou, A. Asymptotically unbiased estimator of the extreme value index under random censoring. Preprint, University of Copenhagen, University of Southern Denmark, Université de Strasbourg. hal-04786783.
Bladt, M., & Øhlenschlæger, C. Censored and extreme losses: functional convergence and applications to tail goodness-of-fit. Preprint, University of Copenhagen. arXiv:2408.05862.
Battagliola, L., & Bladt, M. Extremile scalar-on-function regression with application to climate scenarios. EPFL and University of Copenhagen. arXiv:2405.20817.
Bladt, M., & Rodionov, I. Censored extreme value estimation. Preprint, University of Copenhagen, EPFL and University of Essex. arXiv:2312.10499.
Albrecher, H., Battagliola, L., Bladt, M., Mueller, A., & Swierczynski, T. Flood occurrence in the European Alps: A study over 1000 years based on sediment data. Preprint, University of Copenhagen and University of Lausanne.
Bladt, M., Dias, A., Han, J., & McNeil, A. J. Semiparametric forecasting using non-Gaussian ARMA models based on s-vines. Preprint, University of Copenhagen, University of York.
Bladt, M., & Yslas, J. matrixdist: An R Package for Statistical Analysis of Matrix Distributions. Preprint, University of Lausanne. arXiv:2101.07987.