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Record W4410112053 · doi:10.1080/10920277.2026.2662305

Modeling and A Posteriori Ratemaking for Multiple Perils with the Wishart-Gamma Random Effects Model

2025· preprint· en· W4410112053 on OpenAlexfundno aff
Minjeong Park, Minji Park, Jae Youn Ahn, Lu Yang

Bibliographic record

VenueNorth American Actuarial Journal · 2025
Typepreprint
Languageen
FieldMathematics
TopicStatistical Methods and Inference
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMitacsSociety of Actuaries
KeywordsWishart distributionRandom effects modelEconometricsA priori and a posterioriMathematicsStatisticsEconomicsPhilosophyMultivariate statisticsMedicineEpistemologyMeta-analysis

Abstract

fetched live from OpenAlex

The Wishart-gamma random effects model, introduced by Denuit and Lu (2021), is both flexible and tractable for a variety of insurance applications such as multi-peril ratemaking, frequency-severity ratemaking, and micro-reserving. However, so far, this model has yet to be applied in either the statistical or the actuarial literature, and one important challenge is that its pricing and likelihood

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.056
metaresearch head score (Gemma)0.136
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.056
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.136
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0030.003
Science and technology studies0.0020.006
Scholarly communication0.0070.008
Open science0.0070.006
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0060.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.058
GPT teacher head0.354
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractno

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