Papers


Preprints

  1. Blum, Hiabu, Mammen, Meyer: Consistency of Random Forest Type Algorithms under a Probabilistic Impurity Decrease Condition [arxiv link]
  2. Hiabu, Pittarello, Hofman: A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving [arxiv link] [github]
  3. Hiabu, Mammen, Meyer: Random Planted Forest: a directly interpretable tree ensemble [arxiv link] [github]

Peer-reviewed articles

  1. Liu, Steensgaard, Wright, Pfister, Hiabu (2025): Fast Estimation of Partial Dependence Functions using Trees. ICML.accepted. [arxiv link]
  2. Blum, Hiabu, Mammen, Meyer (2025): Pure interaction effects unseen by Random Forests. Computational Statistics & Data Analysis, 212. [doi] [arxiv link]
  3. Bischofberger, Hiabu, Mammen, Nielsen (2025+): Smooth Backfitting for Additive Hazard Rates. Scandinavian Journal of Statistics. accepted. [doi] [arxiv link]
  4. Pittarello, Hiabu, Villegas (2025+): Replicating and extending chain-ladder via an age-period-cohort structure on the claim development in a run-off triangle. North American Actuarial Journal. accepted. [doi] [arxiv link] [github]
  5. Hiabu, Wilke, Lu (2025+): Identifiability and estimation of the competing risks model under exclusion restrictions. Statistica Neerlandica accepted. [arxiv link]
  6. Hiabu, Mammen, Meyer (2023): Local linear smoothing in additive models as data projection. In: Belomestny, D., Butucea, C., Mammen, E., Moulines, E., Reiß, M., Ulyanov, V.V. (eds). Foundations of Modern Statistics. FMS 2019, Springer. [doi] [arxiv link]
  7. Hiabu, Meyer, Wright (2023): Unifying local and global model explanations by functional decomposition of low dimensional structures. Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, PMLR, 206. [arxiv link] [github]
  8. Hiabu, Nielsen, Scheike (2021): Non-Smooth Backfitting for Excess Risk Additive Regression Model for Survival. Biometrika, 108(2) p. 491-506. [arxiv link] [doi] [github]
  9. Hiabu, Mammen, Martinez-Miranda, Nielsen (2021): Smooth backfitting of proportional hazards with multiplicative components. Journal of the American Statistical Association (Theory&Methods), 116 (536) p. 1983-1993. [arxiv link] [doi] [github]
  10. Gerrard, Hiabu, Nielsen, Vodicka (2020) : Long-term real dynamic investment planning. Insurance: Mathematics and Economics, 92, p. 90-103. [doi]
  11. Bischofberger, Hiabu, Isakson (2020): Continuous chain-ladder with paid data. Scandinavian Actuarial Journal, 2020 (6), p. 477-502. [doi] [arxiv link]
  12. Bischofberger, Hiabu, Mammen, Nielsen (2019): A comparison of in-sample forecasting methods. Computational Statistics and Data Analysis , 137(2019) p. 133-154. [doi]
  13. Gerrard, Hiabu, Kyriakou, Nielsen (2019): Communication and personal selection of pension saver’s financial risk. European Journal of Operational Research , 274(3) p. 1102–1111. [doi] [accepted version]
  14. Gerrard, Hiabu, Kyriakou and Nielsen, (2018): Self selection and risk sharing in a modern world of life long annuities. British Actuarial Journal [accepted version] [discussion]
  15. Hiabu (2017): On the relationship between classical chain ladder and granular reserving. Scandinavian Actuarial Journal , 2017(8) p. 708-729. [doi] [accepted version]
  16. Hiabu, Mammen, Martinez-Miranda, Nielsen (2016) : In-sample forecasting of local linear survival densities. Biometrika , 103(4) p. 843–859 [doi] [accepted version] [supplementary material]
  17. Hiabu, Margraf, Martinez-Miranda, Nielsen (2016): The Link Between Classical Reserving and Granular Reserving Through Double Chain Ladder and its Extensions. British Actuarial Journal , 21(1) p. 97-116. [doi] [accepted version]
  18. Hiabu, Margraf, Martinez-Miranda, Nielsen (2016) : Cash flow generalisations of non-life insurance expert systems estimating outstanding liabilities. Expert Systems With Applications , 45(1) p. 400-409. [doi] [accepted version]
  19. Hiabu, Martinez-Miranda, Nielsen, Spreeuw, Tanggaard, Villegas (2015) : Global Polynomial Kernel Hazard Estimation. Revista Colombiana de Estadístical , 38(2) p. 399-411. [doi] [accepted version]
  20. Agbeko, Hiabu, Martinez-Miranda, Nielsen, Verrall (2014): Validating the Double Chain Ladder Stochastic Claim Reserving Model. Variance , 8(2) p.138-160. [published version]

Other articles

  1. Bladt, Furrer, Hiabu, Steffensen (2023): Matematikken bag bedre forsikringer til en mere fair pris. Aktuel Naturvidenskab 2023(1) [published version]
  2. Fahrenwaldt, Furrer, Hiabu, Huang, Jørgensen, Lindholm, Loftus, Steffensen, Tsanakas (2024): Fairness: plurality, causality, and insurability. European Actuarial Journal, 14, p. 317–328. [doi]
  3. Hiabu, Pittarello, Hofman (2025): Claim Counts Prediction Using Individual Data with ReSurv. CAS E-Forum, 1. [url] [github]