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厦门大学数学科学学院导师教师师资介绍简介-林建希

本站小编 Free考研考试/2021-05-08


林建希
职称:副教授
职务:
学历:博士
电子邮件:jianxilin@xmu.edu.cn
联系电话:**
办 公 室:物机楼513




教育经历: 南开大学基础数学本科
南开大学概率统计硕士
厦门大学基础数学博士
研究方向: 重尾分布及其应用, 精算数学, 应用概率
论文: [1] 林建希, 2007. 关于次指数分布及其相关类的一个性质. 厦门大学学报(自然科学版), 46(4), 461-463.
[2] Lin, Jianxi, 2008. The general principle for precise large deviations of heavy-tailed random sums. Statistics and Probability Letters, 78(6), 749-758.
[3] Lin, Jianxi, 2008. A one-sided large deviation local limit theorem. Statistics and Probability Letters,78, 2679–2684.
[4] Lin, Jianxi, 2008. Some Blackwell-type renewal theorems for weighted renewal functions. Journal of applied probability, 45, 972-993.
[5] 林建希,2010. A Note about local subexponential distributions. 数学研究, 43(4),1-6.
[6] 林建希, 2011. 关于次指数分布性质的一个反例,厦门大学学报(自然科学版),50(6),963-965.
[7] Lin, Jianxi. 2012. Second order subexponential distributions with finite mean and their applications to subordinated distributions. Journal of Theoretical Probability, 25, 834-853.
[8]Lin, Jianxi, Wang, Yuebao. 2012. New examples of heavy-tailed O-subexponential distributions and related closure properties. Statistics and Probability Letters,82, 427-432.
[9]Lin, Jianxi. 2012. Second order asymptotics for ruin probabilities in a renewal risk model with heavy-tailed claims. Insurance: Mathematics and Economics, 51, 422-429.
[10]Lin, Jianxi. 2014. Second order tail behaviour for heavy-tailed sums and their maxima with applications to ruin theory. Extremes. 17, 247–262.
[11]Lin, Jianxi. 2019. Second order tail approximation for the maxima of randomly weighted sums with applications to ruin theory and numerical examples. STATISTICS AND PROBABILITY LETTERS. 153,37-47.
[12]Lin, Jianxi. 2019. Second order tail behaviour of randomly weighted heavy-tailed sums and their maxima. Communications in Statistics - Theory and Methods. doi.org/10.1080/**.2019.**
[13]Lin, Jianxi. 2019. Second order asymptotics for ruin probabilities of the delayed renewal risk model with heavy-tailed claims. Communications in Statistics - Theory and Methods. doi.org/10.1080/**.2019.**






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