Name
Title | ||
|---|---|---|
Non-Nested Estimators For The Central Moments Of A Conditional Expectation And Their Convergence Properties |
Abstract | ||
|---|---|---|
The central moments of a conditional expectation exhibit important statistical properties, and the variance is used in many important applications. This paper proposes three non-nested estimators for the central moments that guarantee the asymptotic mean squared error (MSE) of order Gamma(-1), where Gamma represents the total computational budget. Moreover, the central limit theorems are provided, along with the confidence intervals with a convergence rate of Gamma(-1/2). Examples are used to verify the theoretical results and compare the finite sample performance. (C) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.orl.2021.06.012 | OPERATIONS RESEARCH LETTERS |
Keywords | DocType | Volume |
Nested simulation, Central moments, Non-nested estimation, Convergence rate, Central limit theorems | Journal | 49 |
Issue | ISSN | Citations |
5 | 0167-6377 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong-Fa Cheng | 1 | 0 | 0.34 |
| Kun Zhang | 2 | 0 | 0.34 |