From Point to probabilistic gradient boosting for claim frequency and severity prediction | Paper Citation Record

Observation c3487c1e-696d-4130-b97c-aff1a12a594b · outbound

This paper cites Cambridge University Press, Cambridge (2008).

From Point to probabilistic gradient boosting for claim frequency and severity prediction Cambridge University Press, Cambridge (2008)

Reference 1

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This paper cites Annual Review of Financial Economics 7(1), 253–277 (2015) https://doi.org/10.1146/annurev-financial-111914-041815.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Annual Review of Financial Economics 7(1), 253–277 (2015) https://doi.org/10.1146/annurev-financial-111914-041815

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This paper cites Risks 11(9), 163 (2023) https://doi.org/10.3390/risks11090163.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Risks 11(9), 163 (2023) https://doi.org/10.3390/risks11090163

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This paper cites Statistical Analysis and Data Mining: The ASA Data Science Journal 16(2), 97–119 (2023) https://doi.org/10.1002/sam.11599.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Statistical Analysis and Data Mining: The ASA Data Science Journal 16(2), 97–119 (2023) https://doi.org/10.1002/sam.11599

Reference 5

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This paper cites Insurance: Mathematics and Economics 104, 158–184 (2022) https://doi.org/10.1016/j.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Insurance: Mathematics and Economics 104, 158–184 (2022) https://doi.org/10.1016/j

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This paper cites North American Actuarial Journal 25(1), 53–61 (2020) https://doi.org/10.1080/10920277.2020.1754242.

From Point to probabilistic gradient boosting for claim frequency and severity prediction North American Actuarial Journal 25(1), 53–61 (2020) https://doi.org/10.1080/10920277.2020.1754242

Reference 7

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This paper cites In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.

From Point to probabilistic gradient boosting for claim frequency and severity prediction In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp

Reference 8

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This paper cites Annals of statistics 29, 1189–1232 (2001).

From Point to probabilistic gradient boosting for claim frequency and severity prediction Annals of statistics 29, 1189–1232 (2001)

Reference 9

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This paper cites Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 785–794 (2016) https: //doi.org/10.1145/2939672.2939785.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 785–794 (2016) https: //doi.org/10.1145/2939672.2939785

Reference 10

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This paper cites Advances in neural information processing systems 30, 3146– 3154 (2017).

From Point to probabilistic gradient boosting for claim frequency and severity prediction Advances in neural information processing systems 30, 3146– 3154 (2017)

Reference 11

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This paper cites Advances in neural information processing systems 31, 6639–6649 (2018).

From Point to probabilistic gradient boosting for claim frequency and severity prediction Advances in neural information processing systems 31, 6639–6649 (2018)

Reference 12

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This paper cites Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 150–158 (2012) https://doi.org/10.1145/2339530.2339556.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 150–158 (2012) https://doi.org/10.1145/2339530.2339556

Reference 13

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This paper cites Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 623–631 (2013) https://doi.org/10.1145/2487575.2487579.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 623–631 (2013) https://doi.org/10.1145/2487575.2487579

Reference 14

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This paper cites Annual Review of Statistics and Its Application 9, 119–140 (2022) https://doi.org/10.1146/annurev-statistics-040120-030244.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Annual Review of Statistics and Its Application 9, 119–140 (2022) https://doi.org/10.1146/annurev-statistics-040120-030244

Reference 15

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This paper cites Insurance: Mathematics and Economics 106, 115–127 (2022) https://doi.org/10.1016/j.insmatheco.2022.06.001.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Insurance: Mathematics and Economics 106, 115–127 (2022) https://doi.org/10.1016/j.insmatheco.2022.06.001

Reference 16

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This paper cites Available at SSRN 4352505 (2023) https://doi.org/10.2139/ssrn.4352505.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Available at SSRN 4352505 (2023) https://doi.org/10.2139/ssrn.4352505

Reference 17

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Observation dd9d6927-53ea-427c-80ea-b4d5a7909e8a · outbound

This paper cites Zero-Inflated Tweedie Boosted Trees with CatBoost for Insurance Loss Analytics.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Zero-Inflated Tweedie Boosted Trees with CatBoost for Insurance Loss Analytics

Reference 18

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This paper cites Statistical science 16(3), 199–231 (2001) https://doi.org/10.1214/ss/1009213726 26.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Statistical science 16(3), 199–231 (2001) https://doi.org/10.1214/ss/1009213726 26

Reference 20

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This paper cites XGBoostLSS -- An extension of XGBoost to probabilistic forecasting.

From Point to probabilistic gradient boosting for claim frequency and severity prediction XGBoostLSS -- An extension of XGBoost to probabilistic forecasting

Reference 21

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This paper cites Proceedings of the 37th International Conference on Machine Learning 119, 2690–2700 (2020).

From Point to probabilistic gradient boosting for claim frequency and severity prediction Proceedings of the 37th International Conference on Machine Learning 119, 2690–2700 (2020)

Reference 22

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This paper cites Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 1510–1520 (2021) https://doi.org/10.1145/3447548.3467278.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 1510–1520 (2021) https://doi.org/10.1145/3447548.3467278

Reference 23

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This paper cites Swiss Finance Institute Re- search Paper 16-68 (2023) https://doi.org/10.2139/ssrn.2870308.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Swiss Finance Institute Re- search Paper 16-68 (2023) https://doi.org/10.2139/ssrn.2870308

Reference 24

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This paper cites Springer, Switzerland (2019).

From Point to probabilistic gradient boosting for claim frequency and severity prediction Springer, Switzerland (2019)

Reference 25

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This paper cites Journal of the American Statistical Association 106(494), 494–510 (2011) https://doi.org/10.1198/jasa.2011.ap09272.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Journal of the American Statistical Association 106(494), 494–510 (2011) https://doi.org/10.1198/jasa.2011.ap09272

Reference 26

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This paper cites Extremes 26(4), 639–667 (2023) https://doi.org/10.1007/s10687-023-00473-x.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Extremes 26(4), 639–667 (2023) https://doi.org/10.1007/s10687-023-00473-x

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Journal of the Royal Statistical Society Series C: Applied Statistics 54(3), 507–554 (2005) https://doi.org/ 10.1111/j.1467-9876.2005.00510.x

Reference 28

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Machine learning 5, 197–227 (1990) https://doi

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Taylor & Francis, New York (1984)

Reference 31

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From Point to probabilistic gradient boosting for claim frequency and severity prediction North American Actuarial Journal 22(3), 405–425 (2018) https://doi.org/10.1080/10920277.2018.1431131

Reference 32

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Computational statistics & data analysis 38(4), 367–378 (2002) https://doi.org/10.1016/S0167-9473(01)00065-2

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Journal of Machine Learning Research 38, 489–497 (2015)

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Springer, New York (2009)

Reference 35

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Unresolved cited work

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This paper cites The journal of machine learning research 15(1), 1929–1958 (2014) https://doi.org/10.5555/2627435.2670313.

From Point to probabilistic gradient boosting for claim frequency and severity prediction The journal of machine learning research 15(1), 1929–1958 (2014) https://doi.org/10.5555/2627435.2670313

Reference 37

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Machine learning 45, 5–32 (2001) https://doi.org/10.1023/A: 1010933404324 27

Reference 38

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Unresolved cited work

Reference 39

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Scandinavian Actuarial Journal 10, 1013–1035 (2024) https://doi

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Unresolved cited work

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From Point to probabilistic gradient boosting for claim frequency and severity prediction R package version 1.2.2 (2023)

Reference 42

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Expert Systems With Ap- plications 167, 114080 (2021) https://doi.org/10.1016/j.eswa.2020.114080

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Official Journal of the European Union L 119, 1–88 (2016)

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots

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From Point to probabilistic gradient boosting for claim frequency and severity prediction InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 46

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Journal of the Royal Statistical Society Series B: Statistical Methodology 65(1), 95–114 (2003) https://doi.org/10.1111/1467-9868.00374

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Journal of Statistical Software 74(1), 1–31 (2016) https://doi.org/10

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From Point to probabilistic gradient boosting for claim frequency and severity prediction International series in operations research & management science

Reference 49

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From Point to probabilistic gradient boosting for claim frequency and severity prediction CatBoostLSS -- An extension of CatBoost to probabilistic forecasting

Reference 50

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From Point to probabilistic gradient boosting for claim frequency and severity prediction https://github

Reference 51

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This paper cites Advanced Engineering Informatics 46, 101201 (2020) https://doi.org/10.1016/j.aei.2020.101201.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Advanced Engineering Informatics 46, 101201 (2020) https://doi.org/10.1016/j.aei.2020.101201

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Observation b4d64b2a-636e-4ebb-8c36-8dea638a2d83 · outbound

This paper cites Insurance: Mathematics and Economics 101, 485–497 (2021) https://doi.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Insurance: Mathematics and Economics 101, 485–497 (2021) https://doi

Reference 53

Resolution: verified exact; doi, observed 2026-08-11T11:51:46.343894Z

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Observation 84001cb3-e1a0-4ef4-9498-4af5eb473dfb · outbound

This paper cites Frontier in Econometrics 10, 105–142 (1974) 28.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Frontier in Econometrics 10, 105–142 (1974) 28

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:51:48.205937Z

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Observation 172a7b12-ea97-4739-a889-d041646370ce · outbound

This paper cites Journal of the Royal Statistical Society Series B: Statistical Methodology 78(3), 505–562 (2016) https://doi.org/10.1111/rssb.12154.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Journal of the Royal Statistical Society Series B: Statistical Methodology 78(3), 505–562 (2016) https://doi.org/10.1111/rssb.12154

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T11:51:46.147778Z

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Observation 65ae20bf-4620-45e2-9733-8b9aaa0c0eed · outbound

This paper cites Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T11:51:46.151791Z

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Observation a9c7cbf9-2b9a-4f68-9bfa-1c23f1395749 · outbound

This paper cites North American Actuarial Journal, 1–44 (2025) https://doi.org/10.1080/10920277.2025.2451860.

From Point to probabilistic gradient boosting for claim frequency and severity prediction North American Actuarial Journal, 1–44 (2025) https://doi.org/10.1080/10920277.2025.2451860

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T11:51:46.156347Z

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Observation 5c92c937-db4b-492c-bd1d-a2111a44edcb · outbound

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Monthly Weather Review 133(5), 1098–1118 (2005) https://doi.org/10.1175/MWR2904.1

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T11:51:46.160393Z

Source-reported events for the cited work

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Observation eddb308a-6ccf-4b10-b5e9-fd9c9a8b092c · outbound

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Journal of Statistical Software 90, 1–37 (2019) https://doi.org/10.18637/jss.v090.i12

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T11:51:46.164665Z

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Observation c0606c52-a5a2-4daa-9d72-74a14498e095 · outbound

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Journal of Computational and Graphical Statistics 33, 787–803 (2024) https://doi.org/10.1080/ 10618600.2024.2303336

Reference 60

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Observation ae9d50d7-2a9d-4387-9666-e9244b9f43b8 · outbound

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From Point to probabilistic gradient boosting for claim frequency and severity prediction Insurance: Mathematics and Economics 117, 130–139 (2024) https://doi.org/10

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:51:48.193977Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3d245218-7c2a-41df-89f1-fc3df8ed622f · outbound

This paper cites an unresolved cited work.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:51:48.181586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6d67447a-738a-458e-9dc3-7dd13924e51c · outbound

This paper cites Insurance: Mathematics and Eco- nomics 35(3), 627–647 (2004) https://doi.org/10.1016/j.insmatheco.2004.08.001.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Insurance: Mathematics and Eco- nomics 35(3), 627–647 (2004) https://doi.org/10.1016/j.insmatheco.2004.08.001

Reference 63

Resolution: verified exact; doi, observed 2026-08-11T11:51:46.294627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 60fe2a9d-137c-4024-af9a-61522462f474 · outbound

This paper cites an unresolved cited work.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:51:48.170408Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b23311f0-a9cd-48d3-8174-d90bc70f5a20 · outbound

This paper cites an unresolved cited work.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:51:48.158766Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 84252f8c-f94f-4219-a59c-025e9b736be2 · outbound

This paper cites Expert Systems with Applications 202, 117230 (2022) https://doi.org/10.1016/j.eswa.2022.117230.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Expert Systems with Applications 202, 117230 (2022) https://doi.org/10.1016/j.eswa.2022.117230

Reference 66

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T11:51:46.911381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5b74c775-3efd-4181-ad8a-9b415b464bbe · outbound

This paper cites an unresolved cited work.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:51:48.147450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0151632b-1e22-451a-9281-8d4b0f00b9c9 · outbound

This paper cites North American Actuarial Journal 28(2), 285–319 (2024) https://doi.org/10.1080/10920277.2023.

From Point to probabilistic gradient boosting for claim frequency and severity prediction North American Actuarial Journal 28(2), 285–319 (2024) https://doi.org/10.1080/10920277.2023

Reference 68

Resolution: verified exact; raw_fallback, observed 2026-08-11T11:51:47.786187Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 92f2a46c-9d8a-4d93-8ab9-792bcf5a43e1 · outbound

This paper cites Springer, Cham (2023).

From Point to probabilistic gradient boosting for claim frequency and severity prediction Springer, Cham (2023)

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:51:48.135273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2395261e-0d8d-4497-9033-58c2635367ba · outbound

This paper cites Journal of Risk and Insurance 81(2), 335–366 (2014) https://doi.org/10.1111/j.1539-6975.2012.01507.x.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Journal of Risk and Insurance 81(2), 335–366 (2014) https://doi.org/10.1111/j.1539-6975.2012.01507.x

Reference 70

Resolution: verified exact; raw_fallback, observed 2026-08-11T11:51:46.842684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3f380f57-5649-41f0-88a6-9c1b538bf1b4 · outbound

This paper cites an unresolved cited work.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Unresolved cited work

Reference 71

Resolution: verified exact; doi, observed 2026-08-11T11:51:46.281664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cd939eff-fb9f-4ea2-924f-e1e204433dee · outbound

This paper cites journal of Computational and Graphical Statistics 24(1), 44–65 (2015) https://doi.org/10.1080/10618600.2014.907095.

From Point to probabilistic gradient boosting for claim frequency and severity prediction journal of Computational and Graphical Statistics 24(1), 44–65 (2015) https://doi.org/10.1080/10618600.2014.907095

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T11:51:46.214528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf2dd598-4904-4a3c-a174-d91ff64a298c · outbound

This paper cites The Annals of Applied Statistics 2(3), 916–954 (2008) https://doi.org/10.1214/07-AOAS148.

From Point to probabilistic gradient boosting for claim frequency and severity prediction The Annals of Applied Statistics 2(3), 916–954 (2008) https://doi.org/10.1214/07-AOAS148

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-11T11:51:46.218270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62fb6c54-6b36-4a5a-b238-9fee2b1d8952 · outbound

This paper cites European Actuarial Journal 10(1), 179–202 (2020) https://doi.org/10.1007/s13385-019-00215-z.

From Point to probabilistic gradient boosting for claim frequency and severity prediction European Actuarial Journal 10(1), 179–202 (2020) https://doi.org/10.1007/s13385-019-00215-z

Reference 74

Resolution: verified exact; doi, observed 2026-08-11T11:51:46.260371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0c625d96-96e5-42c2-a9d2-73ae9601a396 · outbound

This paper cites an unresolved cited work.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Unresolved cited work

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-11T11:51:46.226542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Outbound references

Pith citing papers