Technical Note-Central Limit Theorems for Estimated Functions at Estimated Points
成果类型：
Article
署名作者：
Glynn, Peter W.; Fan, Lin; Fu, Michael C.; Hu, Jian-Qiang; Peng, Yijie
署名单位：
Stanford University; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park; Fudan University; Peking University
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.2019.1922
发表日期：
2020
页码：
1557-1563
关键词：
摘要：
We provide a simple proof of the central limit theorem (CLT) for estimated functions at estimated points. Such estimators arise in a number of different simulationbased computational settings. We illustrate the methodology via applications to quantile estimation and related sensitivity analysis, as well as to computation of conditional valueat-risk.