Bayesian CART models for aggregate claim modeling
This paper proposes three types of Bayesian CART (or BCART) models for aggregate claim amount, namely, frequency-severity models, sequential models and joint models. We propose a general framework for the BCART models applicable to data with multivariate responses, which is particularly useful for the joint BCART models with a bivariate response: the number of claims and aggregate claim amount. To facilitate frequency-severity modeling, we investigate BCART models for the right-skewed and heavy-tailed claim severity data by using various distributions. We discover that the Weibull distribution is superior to gamma and lognormal distributions, due to its ability to capture different tail characteristics in tree models. Additionally, we find that sequential BCART models and joint BCART models, which incorporate dependence between the number of claims and average severity, are beneficial and thus preferable to the frequency-severity BCART models in which independence is assumed. The effectiveness of these models' performance is illustrated by carefully designed simulations and real insurance data.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Bayesian CART models for insurance claims frequency
Accuracy and interpretability of a (non-life) insurance pricing model are essential qualities to ensure fair and transparent premiums for policy-holders, that reflect their risk. In recent years, the classification and r…
Data AugmentationModel SelectionOn Mixing Rates for Bayesian CART
The success of Bayesian inference with MCMC depends critically on Markov chains rapidly reaching the posterior distribution. Despite the plentitude of inferential theory for posteriors in Bayesian non-parametrics, conver…
Bayesian InferenceIndividual Claims Forecasting with Bayesian Mixture Density Networks
We introduce an individual claims forecasting framework utilizing Bayesian mixture density networks that can be used for claims analytics tasks such as case reserving and triaging. The proposed approach enables incorpora…
When Should an AI Scientist Stop? Verifiable Experiment Steering and Refusal for Autonomous Discovery
We present CARTOGRAPH, a verification layer for AI scientists that couples unresolved-subspace experiment steering (select), explicit ambiguity closure (resolve), and residual-based library inadequacy detection (refuse).…
[Re] On the Reproducibility of CartoonX
Scope of Reproducibility — CartoonX [1] is a novel explanation method for image classifiers. In this reproducibility study, we examine the claims of the original authors of CartoonX that it: (i) extracts relevant piece‐w…