Matched-block bootstrap for dependent data
TLDR
In this article, the authors proposed a matching algorithm based on a kernel estimate of the conditional lag one distribution or on a fitted autoregression of small order to align with higher likelihood those blocks which match at their ends.Abstract:
The block bootstrap for time series consists in randomly resampling blocks of consecutive values of the given data and aligning these blocks into a bootstrap sample. Here we suggest improving the performance of this method by aligning with higher likelihood those blocks which match at their ends. This is achieved by resampling the blocks according to a Markov chain whose transitions depend on the data. The matching algorithms that we propose take some of the dependence structure of the data into account. They are based on a kernel estimate of the conditional lag one distribution or on a fitted autoregression of small order. Numerical and theoretical analysis in the case of estimating the variance of the sample mean show that matching reduces bias and, perhaps unexpectedly, has relatively little effect of variance. Our theory extends to the case of smooth functions of a vector mean.read more
Citations
More filters
Journal ArticleDOI
Colored noise and computational inference in neurophysiological (fMRI) time series analysis: Resampling methods in time and wavelet domains
Edward T. Bullmore,Edward T. Bullmore,Christopher J. Long,John Suckling,Jalal M. Fadili,Gemma A. Calvert,Fernando Zelaya,T. Adrian Carpenter,Mick Brammer +8 more
TL;DR: It is concluded that wavelet resampling may be a generally useful method for inference on naturally complex time series based on random permutation after orthogonal transformation of the observed time series to the wavelet domain.
Journal ArticleDOI
Bootstrap Methods For Time Series
TL;DR: It is argued that methods for implementing the bootstrap with time‐series data are not as well understood as methods for data that are independent random samples, and there is a considerable need for further research.
Journal ArticleDOI
Recent Developments in Bootstrapping Time Series
Jeremy Berkowitz,Lutz Kilian +1 more
TL;DR: It is shown that the block size plays an important role in determining the success of the block bootstrap, and a data-based block size selection procedure is proposed, which would account for lag order uncertainty in resampling.
Journal ArticleDOI
Bootstraps for Time Series
TL;DR: It is argued that two types of sieves outperform the block method, each of them in its own important niche, namely linear and categorical processes, respectively.
Book
Climate Time Series Analysis: Classical Statistical and Bootstrap Methods
TL;DR: In this article, the authors introduce persistence models and Bootstrap Confidence Intervals for univariate and bivariate time series analysis, and present a future direction for future directions. But, they do not discuss the use of spectral analysis.
References
More filters
BookDOI
Density estimation for statistics and data analysis
TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Journal ArticleDOI
Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
Journal ArticleDOI
The stationary bootstrap
TL;DR: In this paper, the stationary bootstrap technique was introduced to calculate standard errors of estimators and construct confidence regions for parameters based on weakly dependent stationary observations, where m is fixed.
Journal ArticleDOI
The Jackknife and the Bootstrap for General Stationary Observations
TL;DR: In this article, the authors extend the jackknife and the bootstrap method of estimating standard errors to the case where the observations form a general stationary sequence, and they show that consistency is obtained if $l = l(n) \rightarrow \infty$ and $l(n)/n \ rightarrow 0$.
Journal ArticleDOI
The Use of Subseries Values for Estimating the Variance of a General Statistic from a Stationary Sequence
TL;DR: In this article, the authors proposed a variance estimator for a general statistic, where the subseries values are used as replicates to model the sampling variability of the sample variance.