.png){kind=icon}
返回
Simple resampling methods for censored regression quantiles
.png){kind=icon}
PRE
AI
DOI：10.1016/S0304-4076(00)00042-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Powell (Journal of Econometrics 25 (1984) 303-325; journal of Econometrics 32 (1986) 143-155) considered censored regression quantile estimators. The asymptotic covariance matrices of his estimators depend on the error densities and are therefore difficult to estimate reliably. The difficulty may be avoided by applying the bootstrap method (Hahn, Econometric Theory 11 (1995) 105-121). Calculation of the estimators, however, requires solving a nonsmooth and nonconvex minimization problem, resulting in high computational costs in implementing the bootstrap, We propose in this paper computationally simple resampling methods by convexfying Powell's approach in the resampling stage. A major advantage of the new methods is that they can be implemented by efficient linear programming. Simulation studies show that the methods are reliable even with moderate sample sizes. (C) 2000 Elsevier Science S.A. All rights reserved. JEL classification: C14; C24.
Keyword:
censored regression quantiles
least absolute deviation
linear programming
resampling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
5.2K
被引数:
3.0W
机构
暂无机构信息
引用论文
.png){kind=icon}
Recent advances in quantile regression models - A practical guideline for empirical research分位数回归模型的最新进展-实证研究的实用指南
PREAI
分享收藏
查看更多内容