Yunyun Wang, Tatsushi Oka, Dan Zhu
arXiv 23 Mar 2022 · Statistics — Methodology · publishedInsurance Mathematics and Economics (2023) · 5 citations (OpenAlex)
arXiv:2203.12228 · PDF · DOI · OpenAlex · Extracted main text
Understanding variable dependence, particularly eliciting their statistical properties given a set of covariates, provides the mathematical foundation in practical operations management such as risk analysis and decision-making given observed circumstances. This article presents an estimation method for modeling the conditional joint distribution of bivariate outcomes based on the distribution regression and factorization methods. This method is considered semiparametric in that it allows for flexible modeling of both the marginal and joint distributions conditional on covariates without imposing global parametric assumptions across the entire distribution. In contrast to existing parametric approaches, our method can accommodate discrete, continuous, or mixed variables, and provides a simple yet effective way to capture distributional dependence structures between bivariate outcomes and covariates. Various simulation results confirm that our method can perform similarly or better in finite samples compared to the alternative methods. In an application to the study of a motor third-party liability insurance portfolio, the proposed method effectively estimates risk measures such as the conditional Value-at-Risk and Expected Shortfall. This result suggests that this semiparametric approach can serve as an alternative in insurance risk management.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Czado, C., Kastenmeier, R., Brechmann, E.C., Min, A (2012) A mixed copula model for insurance claims and claim sizes | 0.928 | 4 | 3 | 100% |
| 2 | Chernozhukov, V., Fernández-Val, I., Melly, B (2013) Inference on counterfactual distributions | 0.874 | 5 | 2 | 100% |
| 3 | Garrido, J., Genest, C., Schulz, J (2016) Generalized linear models for dependent frequency and severity of insurance claims | 0.843 | 3 | 3 | 100% |
| 4 | Yang, L (2020) Nonparametric copula estimation for mixed insurance claim data | 0.737 | 3 | 2 | 100% |
| 5 | van der Vaart, A., Wellner, J (1996) Weak convergence and empirical processes: with applications to statistics | 0.693 | 6 | 2 | 50% |
| 6 | White, H (1982) Maximum likelihood estimation of misspecified models | 0.644 | 2 | 2 | 100% |
| 7 | Chernozhukov, V., Fernandez-Val, I., Galichon, A (2009) Improving point and interval estimators of monotone functions by rearrangement | 0.644 | 2 | 2 | 100% |
| 8 | Klein, N., Hothorn, T., Barbanti, L., Kneib, T (2022) Multivariate conditional transformation models | 0.644 | 2 | 2 | 100% |
| 9 | Meier, J (2020) Multivariate Distribution Regression | 0.644 | 2 | 2 | 100% |
| 10 | Praestgaard, J., Wellner, J.A (1993) Exchangeably weighted bootstraps of the general empirical process | 0.644 | 2 | 2 | 100% |
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