Robust claim frequency modeling through phase-type mixture-of-experts regression
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DOI: 10.1016/j.insmatheco.2023.02.008
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- 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.
- Qi-Ming He & Jiandong Ren, 2016. "Parameter Estimation of Discrete Multivariate Phase-Type Distributions," Methodology and Computing in Applied Probability, Springer, vol. 18(3), pages 629-651, September.
- Gabrielli, Andrea, 2020. "A Neural Network Boosted Double Overdispersed Poisson Claims Reserving Model," ASTIN Bulletin, Cambridge University Press, vol. 50(1), pages 25-60, January.
- Frees, Edward W. (Jed) & Meyers, Glenn & Cummings, A. David, 2010. "Dependent Multi-Peril Ratemaking Models," ASTIN Bulletin, Cambridge University Press, vol. 40(2), pages 699-726, November.
- Chai Fung, Tsz & Badescu, Andrei L. & Sheldon Lin, X., 2019. "A Class Of Mixture Of Experts Models For General Insurance: Application To Correlated Claim Frequencies," ASTIN Bulletin, Cambridge University Press, vol. 49(3), pages 647-688, September.
- Edward Frees, 2003. "Multivariate Credibility for Aggregate Loss Models," North American Actuarial Journal, Taylor & Francis Journals, vol. 7(1), pages 13-37.
- Qi-Ming He & Jiandong Ren, 2016. "Analysis of a Multivariate Claim Process," Methodology and Computing in Applied Probability, Springer, vol. 18(1), pages 257-273, March.
- Yip, Karen C.H. & Yau, Kelvin K.W., 2005. "On modeling claim frequency data in general insurance with extra zeros," Insurance: Mathematics and Economics, Elsevier, vol. 36(2), pages 153-163, April.
- Lu Yang & Peng Shi, 2019. "Multiperil rate making for property insurance using longitudinal data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 647-668, February.
- Zhang, Pengcheng & Pitt, David & Wu, Xueyuan, 2022. "A New Multivariate Zero-Inflated Hurdle Model With Applications In Automobile Insurance," ASTIN Bulletin, Cambridge University Press, vol. 52(2), pages 393-416, May.
- Hansjörg Albrecher & Mogens Bladt & Jorge Yslas, 2022. "Fitting inhomogeneous phase‐type distributions to data: the univariate and the multivariate case," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 44-77, March.
- Edward W. Frees & Gee Lee & Lu Yang, 2016. "Multivariate Frequency-Severity Regression Models in Insurance," Risks, MDPI, vol. 4(1), pages 1-36, February.
- Fung, Tsz Chai & Badescu, Andrei L. & Lin, X. Sheldon, 2019. "A class of mixture of experts models for general insurance: Theoretical developments," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 111-127.
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More about this item
Keywords
Discrete phase-type distributions; Regression modeling; Claim count distributions;All these keywords.
JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
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