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Risk Factor Selection in Rate Making: EM Adaptive LASSO for Zero‐Inflated Poisson Regression Models

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  • Yanlin Tang
  • Liya Xiang
  • Zhongyi Zhu

Abstract

Risk factor selection is very important in the insurance industry, which helps precise rate making and studying the features of high‐quality insureds. Zero‐inflated data are common in insurance, such as the claim frequency data, and zero‐inflation makes the selection of risk factors quite difficult. In this article, we propose a new risk factor selection approach, EM adaptive LASSO, for a zero‐inflated Poisson regression model, which combines the EM algorithm and adaptive LASSO penalty. Under some regularity conditions, we show that, with probability approaching 1, important factors are selected and the redundant factors are excluded. We investigate the finite sample performance of the proposed method through a simulation study and the analysis of car insurance data from SAS Enterprise Miner database.

Suggested Citation

  • Yanlin Tang & Liya Xiang & Zhongyi Zhu, 2014. "Risk Factor Selection in Rate Making: EM Adaptive LASSO for Zero‐Inflated Poisson Regression Models," Risk Analysis, John Wiley & Sons, vol. 34(6), pages 1112-1127, June.
    • Handle: RePEc:wly:riskan:v:34:y:2014:i:6:p:1112-1127
    DOI: 10.1111/risa.12162
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    References listed on IDEAS

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    Cited by:

    1. Chen, Kun & Huang, Rui & Chan, Ngai Hang & Yau, Chun Yip, 2019. "Subgroup analysis of zero-inflated Poisson regression model with applications to insurance data," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 8-18.
    2. Li Xiang & Hu Xuemei & Yang Junwen, 2023. "Regularized Poisson regressions predict regional innovation output," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2197-2216, December.

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