EFFICIENT ESTIMATION OF ERLANG MIXTURES USING iSCAD PENALTY WITH INSURANCE APPLICATION

被引：15

作者：

Yin, Cuihong ^{[1
]}

Lin, X. Sheldon ^{[1
,2
]}

机构：

[1] Xiamen Univ, Sch Math Sci, Xiamen, Peoples R China

[2] Univ Toronto, Dept Stat Sci, Toronto, ON M5S 3G3, Canada

来源：

ASTIN BULLETIN | 2016年 / 46卷 / 03期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Erlang mixture; EM algorithm; iSCAD penalty; VaR; TVaR; SELECTION; SURPLUS; IDENTIFIABILITY; DISTRIBUTIONS; MOMENTS; DEFICIT; MODELS; RISKS; ORDER; TIME;

D O I：

10.1017/asb.2016.14

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

The Erlang mixture model has been widely used in modeling insurance losses due to its desirable distributional properties. In this paper, we consider the problem of efficient estimation of the Erlang mixture model. We present a new thresholding penalty function and a corresponding EM algorithm to estimate model parameters and to determine the order of the mixture. Using simulation studies and a real data application, we demonstrate the efficiency of the EM algorithm.

引用

页码：778 / 798

页数：21

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