Paper Citation Record · LEDGER

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance

As of 17 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.05609.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.05609 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:18:35.824380Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 01932267-b460-4e6c-beb2-77af4666a752 · outbound

This paper cites Procura por seguro viagem dis- para no primeiro bimestre de 2024,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Procura por seguro viagem dis- para no primeiro bimestre de 2024,

Reference 1

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Source-reported events for the cited work

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Observation 3766a769-152b-4389-a16f-ae04f3dab9da · outbound

This paper cites Tendências na indústria de seguros 2023,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Tendências na indústria de seguros 2023,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:42.261867Z

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Observation c2194764-2ec8-4ab3-935a-7064028bcfd7 · outbound

This paper cites Predicting travel insurance pur- chases in an insurance firm through machine learning methods after covid-19,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Predicting travel insurance pur- chases in an insurance firm through machine learning methods after covid-19,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:42.060461Z

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Observation ce7d63b8-3128-4436-bdb7-25362126d0d1 · outbound

This paper cites Exploring the potential of machine learning techniques for predicting travel insurance claims: A comparative analysis of four models,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Exploring the potential of machine learning techniques for predicting travel insurance claims: A comparative analysis of four models,

Reference 4

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Observation bf0303e6-1768-413d-a801-32a62f200b05 · outbound

This paper cites Insurance risk prediction using machine learning,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Insurance risk prediction using machine learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:41.583238Z

Source-reported events for the cited work

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Observation b2dde2ab-5b28-418c-8fee-7e1e7657f16c · outbound

This paper cites Research on changes in travel insurance premiums driven by climate change: A case study of hong kong region,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Research on changes in travel insurance premiums driven by climate change: A case study of hong kong region,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:41.367515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a963a0d9-2e96-49f6-b858-e42b14a65f58 · outbound

This paper cites A machine-learning-based business ana- lytical system for insurance customer relationship management and cross-selling,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance A machine-learning-based business ana- lytical system for insurance customer relationship management and cross-selling,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:41.124085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 137491a0-5390-4335-8047-67a1f7a7e315 · outbound

This paper cites Random forests,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Random forests,

Reference 8

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Source-reported events for the cited work

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Observation 5999a158-9838-4d5c-8808-182a6dd561c8 · outbound

This paper cites Xgboost: A scalable tree boosting system,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Xgboost: A scalable tree boosting system,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:40.767278Z

Source-reported events for the cited work

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Observation a27194d9-26a0-4ee1-a13b-1f76864952fa · outbound

This paper cites Lightgbm: A highly effi- cient gradient boosting decision tree,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Lightgbm: A highly effi- cient gradient boosting decision tree,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:40.622180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e4b89873-5df9-4c93-96f4-caacde48a578 · outbound

This paper cites glmnet: Lasso and elastic-net regularized generalized linear models,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance glmnet: Lasso and elastic-net regularized generalized linear models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:40.433387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c57f1390-ca19-47e0-a3e6-bfd7101d9be6 · outbound

This paper cites R: A language and environment for statistical computing,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance R: A language and environment for statistical computing,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:40.278778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9c793839-b711-486e-b817-f55edd915ae4 · outbound

This paper cites Regularization paths for generalized lin- ear models via coordinate descent,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Regularization paths for generalized lin- ear models via coordinate descent,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:40.011774Z

Source-reported events for the cited work

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Observation 5c9e009c-0232-4b5d-a02e-6859786129ad · outbound

This paper cites Regression shrinkage and selection via the lasso,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Regression shrinkage and selection via the lasso,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:39.838410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 08461fea-8987-4b75-92eb-fc74a9f9a0f4 · outbound

This paper cites Regularization and variable selection via the elastic net,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Regularization and variable selection via the elastic net,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:39.627911Z

Source-reported events for the cited work

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Observation c7cf3961-2706-4b1b-a403-e26fe8d9ff71 · outbound

This paper cites Random search for hyper-parameter optimization,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Random search for hyper-parameter optimization,

Reference 16

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Source-reported events for the cited work

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Observation e41a3487-184c-42bc-accf-a8ae9f17149a · outbound

This paper cites Algorithms for hyper-parameter optimization,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Algorithms for hyper-parameter optimization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:39.175985Z

Source-reported events for the cited work

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Observation 8bdf1692-de7a-406a-8544-0b22446a6658 · outbound

This paper cites Ridge regression: Biased estimation for nonorthog- onal problems,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Ridge regression: Biased estimation for nonorthog- onal problems,

Reference 18

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Observation c23470a9-21ff-4bff-8822-04f80200c128 · outbound

This paper cites A study of cross-validation and bootstrap for accuracy estimation and model selection,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance A study of cross-validation and bootstrap for accuracy estimation and model selection,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:38.540866Z

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Observation 336cff41-edaa-4fdf-9c46-acc15db8228d · outbound

This paper cites The use of the area under the roc curve in the evaluation of machine learning algorithms,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance The use of the area under the roc curve in the evaluation of machine learning algorithms,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:38.296397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ccc19320-c81f-469d-b646-7eab780ddcd9 · outbound

This paper cites Regularization paths for generalized lin- ear models via coordinate descent,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Regularization paths for generalized lin- ear models via coordinate descent,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:37.924313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9d32b787-1b1b-4dd9-a7a3-40a1cfd82a6b · outbound

This paper cites Hastie, R.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Hastie, R

Reference 22

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This paper cites James, D.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance James, D

Reference 23

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Source-reported events for the cited work

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Observation 6b0fe3cf-457b-4b62-ac9d-fc279baba05a · outbound

This paper cites CatBoost: gradient boosting with categorical features support.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance CatBoost: gradient boosting with categorical features support

Reference 24

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Source-reported events for the cited work

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Observation dcf74b62-1691-477a-a332-e611a9dd47cc · outbound

This paper cites Catboost: Unbiased boosting with categorical features,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Catboost: Unbiased boosting with categorical features,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:37.273949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b5b53990-55d7-42e3-9e72-6ab3486d9ea0 · outbound

This paper cites Permutation importance: a cor- rected feature importance measure,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Permutation importance: a cor- rected feature importance measure,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:36.996939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fd34c0a7-6186-48b6-a609-44977eba7647 · outbound

This paper cites A unified approach to interpreting model predictions,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance A unified approach to interpreting model predictions,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:36.768695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a4aa7c9f-e8ee-45b5-9ed0-1ab2d3904d23 · outbound

This paper cites Hybrid non-heuristic variable selection models via regularization for black box models applied to the insurance sector,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Hybrid non-heuristic variable selection models via regularization for black box models applied to the insurance sector,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:36.481260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 25f13f30-6ee9-4528-a56f-33fbfcb78f86 · outbound

This paper cites Multivariate adaptive regression splines,.

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance Multivariate adaptive regression splines,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:36.193036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

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