This paper gives a review on a variety of bootstrap methods for dependent data. The main
focus is not on an exhaustive listing and description of bootstrap procedures but on general …

This is the second edition of Climate Time Series Analysis which was first published in 2010.
In this digital age, a second edition means not that the first edition of a book has been sold …

Sparked by Efron's seminal paper, the decade of the 1980s was a period of active research
on bootstrap methods for independent data-mainly iid or regression set-ups. By contrast, in …

We review and compare block, sieve and local bootstraps for time series and thereby
illuminate theoretical aspects of the procedures as well as their performance on finite …

The bootstrap is a method for estimating the distribution of an estimator or test statistic by
resampling one's data or a model estimated from the data. The methods that are available …

A comprehensive introduction to a wide variety of univariate and multivariate smoothing
techniques for regression Smoothing and Regression: Approaches, Computation, and …

The chapter gives a review of the literature on bootstrap methods for time series data. It
describes various possibilities on how the bootstrap method, initially introduced for …

The paper considers construction of simultaneous confidence tubes for time varying
regression coefficients in functional linear models. Using a Gaussian approximation result …

Kernel smoothing in nonparametric autoregressive schemes offers a powerful tool in
modelling time series. We show that the bootstrap can be used for estimating the distribution …

Knowledge about the distribution of a statistical estimator is important for various purposes,
such as the construction of confidence intervals for model parameters or the determination of …