Technical Note—Central Limit Theorems for Estimated Functions at Estimated Points

Autor:	Lin Fan, Peter W. Glynn, Michael C. Fu, Yijie Peng, Jian-Qiang Hu
Rok vydání:	2020
Předmět:	Data set Applied mathematics Technical note Sample (statistics) Point (geometry) Function (mathematics) Management Science and Operations Research Value (mathematics) Computer Science Applications Mathematics Central limit theorem
Zdroj:	Operations Research. 68:1557-1563
ISSN:	1526-5463 0030-364X
DOI:	10.1287/opre.2019.1922
Popis:	The need to estimate a function value from sample data at a point that is itself estimated from the same data set arises in many application settings. Such applications include value-at-risk, conditional value-at-risk, and other so-called distortion risk measures. In this note, Peter W. Glynn, Lin Fan, Michael C. Fu, Jian-Qiang Hu, and Yijie Peng provide a simple proof for a central limit theorem for such estimators, and provide an accompanying batching/sectioning methodology that can be used to construct large-sample confidence intervals in the presence of such estimators.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::049f9680ceb700311fbf277aacef7b97 https://doi.org/10.1287/opre.2019.1922 Zobrazit plný text záznamu