.png){kind=icon}
返回
.png){kind=icon}
.png){kind=icon}
摘要
En 中文
The block bootstrap is the best known bootstrap method for time-series data when the analyst does not have a parametric model that reduces the data generation process to simple random sampling. However, the errors made by the block bootstrap converge to zero only slightly faster than those made by first-order asymptotic approximations. This paper describes a bootstrap procedure for data that are generated by a Markov process or a process that can be approximated by a Markov process with sufficient accuracy. The procedure is based on estimating the Markov transition density nonparametrically. Bootstrap samples are obtained by sampling the process implied by the estimated transition density. Conditions are given under which the errors made by the Markov bootstrap converge to zero more rapidly than those made by the block bootstrap.
Keyword:
time series
resampling
asymptotic refinement
Edgeworth expansion
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:
7.1
论文数:
3.0K
被引数:
4.3W
机构
暂无机构信息
引用论文
PECVD-grown carbon nanotubes on silicon substrates with a nickel-seeded tip-growth structure具有镍种子尖端生长结构的硅衬底上的PECVD生长的碳纳米管.png)
PREAI
.png){kind=icon}
分享.png){kind=icon}
收藏AN IMPROVED HETEROSKEDASTICITY AND AUTOCORRELATION CONSISTENT COVARIANCE-MATRIX ESTIMATOR.png)
ECONOMETRICA
ECONOMETRICAIF7.1
PREAI
分享收藏
查看更多内容