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高光远

职 称: 讲师


职 务:


电子邮箱: guangyuan.gao@ruc.edu.cn

教育经历

澳洲国立大学 统计 博士
澳洲国立大学 应用统计 硕士
同济大学 工学 学士

工作经历

2016.09-至今 中国人民大学 讲师

研究方向

车联网数据分析;非寿险准备金评估模型;贝叶斯模型;MCMC

论文成果

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代表论文:
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>> Gao, G. and Wuthrich, M. V.*(2019). Convolutional neural network classification of telematics
car driving data. Risks, 7(1): article 6.
>> Gao, G.*, Meng, S. and Wuthrich, M. V. (2019). Claims frequency modeling using telematics car
driving data. Scandinavian Actuarial Journal, 2019(2): 143-162.
>> Gao, G.* and Meng, S. (2018). Stochastic claims reserving via a Bayesian spline model with
random loss ratio effects. ASTIN Bulletin, 48(1): 55-88.
>> 高光远*; 孟生旺(2018). 基于车联网大数据的车险费率因子研究. 保险研究, 357(1): 90-100.
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其他论文:
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>> Gao, G., Meng, S.* and Shi, Y. (2019). Stochastic payments per claim incurred. North American
Actuarial Journal, DOI: 10.1080/10920277.2018.1480390.
>> Gao, G., Ho, K.-Y. and Shi, Y.* (2018). Long memory or regime switching in volatility? Evidence
from high-frequency returns on the U.S. stock indices. Pacific-Basin Finance Journal, DOI:
10.1016/j.pacfin.2018.08.013.
>> Gao, G. and Wuthrich, M. V.* (2018). Feature extraction from telematics car driving heatmaps.
European Actuarial Journal, 8(2): 383-406.
>> Meng, S. and Gao, G.* (2018). Compound Poisson claims reserving models: Extensions and
inference. ASTIN Bulletin, 48(3): 1137-1156.
>> 孟生旺*; 李天博; 高光远(2017). 基于机器学习算法的车险索赔概率与累积赔款预测. 保险研究,
354(10):42-53.


著作成果

Gao, G. (2018). Bayesian Claims Reserving Methods in Non-life Insurance with Stan: An Introduction. Springer, Singapore. DOI: 10.1007/978-981-13-3609-6.