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Subsampling confidence intervals for parameters of atmospheric time series: Block size choice and calibration
(2005)
Problems of practical implementation of the computer intensive subsampling methodology are addressed by Monte Carlo simulations of a situation typical for atmospheric time series. The motivating data were collected under ...
Generalized seasonal tapered block bootstrapAAA
(2016)
In this paper a new block bootstrap method for periodic time series called Generalized Seasonal Tapered Block Bootstrap (GSTBB) is introduced. Consistency of the GSTBB for parameters associated with periodically correlated ...
Generalized seasonal tapered block bootstrap
(2016)
In this paper a new block bootstrap method for periodic time series called Generalized Seasonal Tapered Block Bootstrap (GSTBB) is introduced. Consistency of the GSTBB for parameters associated with periodically correlated ...
Subsampling Inference with K Populations and a Non-standard Behrens-Fisher Problem
(2012)
We revisit the methodology and historical development of subsampling, and then explore in detail its use in hypothesis testing, an area which has received surprisingly modest attention. In particular, the general set-up ...
The local bootstrap for kernel estimators under general dependence conditions
(2000)
We consider the problem of estimating the distribution of a nonparametric (kernel) estimator of the conditional expectation g(Greek cursive chi
Large-sample inference in the general AR(1) model
(2000)
The situation where the available data arise from a general AR(1) model is discussed, and two new avenues for constructing confidence intervals for the unknown autoregressive root are proposed, one based on a Central Limit ...
Bootstrap prediction intervals for Markov processes
(2014)
Given time series data X1,…,Xn, the problem of optimal prediction of Xn+1 has been well-studied. The same is not true, however, as regards the problem of constructing a prediction interval with prespecified coverage ...
Bootstrap prediction intervals for linear, nonlinear and nonparametric autoregressions
(2016)
In order to construct prediction intervals without the cumbersome-and typically unjustifiable-assumption of Gaussianity, some form of resampling is necessary. The regression set-up has been well-studied in the literature ...
Large-sample inference in the general AR(1) modelAAA
(2000)
The situation where the available data arise from a general AR(1) model is discussed, and two new avenues for constructing confidence intervals for the unknown autoregressive root are proposed, one based on a Central Limit ...
The Impact of Bootstrap Methods on Time Series Analysis
(2003)
Sparked by Efron's seminal paper, the decade of the 1980s was a period of active research on bootstrap methods for independent data - mainly i.i.d. or regression set-ups. By contrast, in the 1990s much research was directed ...