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Higher-order accurate polyspectral estimation with flat-top lag-windows
(2009)
Improved performance in higher-order spectral density estimation is achieved using a general class of infinite-order kernels. These estimates are asymptotically less biased but with the same order of variance as compared ...
Testing Time Series Linearity. Traditional and Bootstrap Methods
(2012)
We review the notion of time series linearity and describe recent advances in linearity and Gaussianity testing via data resampling methodologies. Many advances have been made since the first published tests of linearity ...
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 ...
Valid resampling of higher-order statistics using the linear process bootstrap and autoregressive sieve bootstrap
(2013)
We show that the linear process bootstrap (LPB) and the autoregressive sieve bootstrap (AR sieve) are, in general, not valid for statistics whose large-sample distribution depends on moments of order higher than two, ...
Baxter’s inequality for triangular arrays
(2015)
A central problem in time series analysis is prediction of a future observation. The theory of optimal linear prediction has been well understood since the seminal work of A. Kolmogorov and N. Wiener during World War II. ...
AAA Note on the Behaviour of Nonparametric Density and Spectral Density Estimators at Zero Points of their Support
(2015)
The asymptotic behaviour of nonparametric estimators of the stationary density and of the spectral density function of a stationary process have been studied in some detail in the last 50-60years. Nevertheless, less is ...
Bootstrap technology and applications
(1992)
Bootstrap resampling methods have emerged as powerful tools for constructing inferential procedures in modern statistical data analysis. Although these methods depend on the availability of fast, inexpensive computing, ...
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 ...
Distribution theory for the studentized mean for long, short, and negative memory time series
(2013)
We consider the problem of estimating the variance of the partial sums of a stationary time series that has either long memory, short memory, negative/intermediate memory, or is the first-difference of such a process. The ...