Blum, Hiabu, Mammen, Meyer: Hidden Variables unseen by Random Forests
[arxiv link]
Blum, Hiabu, Mammen, Meyer: Consistency of Random Forest Type Algorithms under a Probabilistic Impurity Decrease Condition
[arxiv link]
Hiabu, Wilke, Lu: Identifiability and estimation of the competing risks model under exclusion restrictions
[arxiv link]
Hiabu, Pittarello, Hofman: A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving
[arxiv link][github]
Pittarello, Hiabu, Villegas: Replicating and extending chain-ladder via an age-period-cohort structure on the claim development in a run-off triangle
[arxiv link][github]
Hiabu, Mammen, Meyer: Random Planted Forest: a directly interpretable tree ensemble [arxiv link][github]
Peer-reviewed articles
Fahrenwaldt, Furrer, Hiabu, Huang, Jørgensen, Lindholm, Loftus, Steffensen, Tsanakas (2024): Fairness: plurality, causality, and insurability.
European Actuarial Journal[doi]
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]
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]
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]
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]
Gerrard, Hiabu, Nielsen, Vodicka (2020) : Long-term real dynamic investment planning.
Insurance: Mathematics and Economics, 92, p. 90-103
[doi]
Bischofberger, Hiabu, Isakson (2020): Continuous chain-ladder with paid data.
Scandinavian Actuarial Journal, 2020 (6), p. 477-502
[doi][arxiv link]
Bischofberger, Hiabu, Mammen, Nielsen (2019): A comparison of in-sample forecasting methods.
Computational Statistics and Data Analysis ,
137(2019) p. 133-154
[doi]
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]
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]
Hiabu (2017): On the relationship between classical chain ladder and granular reserving.
Scandinavian Actuarial Journal , 2017(8) p. 708-729
[doi][accepted version]
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]
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]
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]