英文标题：
《Asyptotic Normality for Maximum Likelihood Estimation and Operational
Risk》
---
作者：
Paul Larsen
---
最新提交年份：
2016
---
英文摘要：
Operational risk models commonly employ maximum likelihood estimation (MLE) to fit loss data to heavy-tailed distributions. Yet several desirable properties of MLE (e.g. asymptotic normality) are generally valid only for large sample-sizes, a situation rarely encountered in operational risk. In this paper, we study how asymptotic normality does--or does not--hold for common severity distributions in operational risk models. We then apply these results to evaluate errors caused by failure of asymptotic normality in constructing confidence intervals around the MLE fitted parameters.
---
中文摘要：
操作风险模型通常采用最大似然估计（MLE）将损失数据拟合为重尾分布。然而，最大似然估计的几个理想性质（如渐近正态性）通常只适用于大样本量，这种情况在操作风险中很少遇到。在本文中，我们研究了操作风险模型中常见严重性分布的渐近正态性是如何成立的。然后，我们应用这些结果来评估由于在MLE拟合参数周围构造置信区间时出现渐近正态性故障而导致的误差。
---
分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
--
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
--

---
PDF下载：
-->