Browsing by Subject "Bootstrap"
Now showing items 1-20 of 34
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Autoregressive-aided periodogram bootstrap for time series
(2003)A bootstrap methodology for the periodogram of a stationary process is proposed which is based on a combination of a time domain parametric and a frequency domain nonparametric bootstrap. The parametric fit is used to ...
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Banded and tapered estimates for autocovariance matrices and the linear process bootstrap
(2010)We address the problem of estimating the autocovariance matrix of a stationary process. Under short range dependence assumptions, convergence rates are established for a gradually tapered version of the sample autocovariance ...
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A bootstrap test for time series linearity
(2010)A bootstrap algorithm is proposed for testing Gaussianity and linearity in stationary time series, and consistency of the relevant bootstrap approximations is proven rigorously for the first time. Subba Rao and Gabr (1980) ...
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Bootstrap-based testing of equality of mean functions or equality of covariance operators for functional data
(2016)We investigate the properties of a simple bootstrap method for testing the equality of mean functions or of covariance operators in functional data. Theoretical size and power results are derived for certain test statistics, ...
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Bootstrapping autoregressive and moving average parameter estimates of infinite order vector autoregressive processes
(1996)We consider an r-dimensional multivariate time series {yt, t∈Z} which is generated by an infinite order vector autoregressive process. We show that a bootstrap procedure which works by generating time series replicates via ...
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Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities
(2009)We propose a general bootstrap procedure to approximate the null distribution of non-parametric frequency domain tests about the spectral density matrix of a multivariate time series. Under a set of easy-to-verify conditions, ...
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Bootstrapping locally stationary processes
(2015)We propose a non-parametric method to bootstrap locally stationary processes which combines a time domain wild bootstrap approach with a non-parametric frequency domain approach. The method generates pseudotime series which ...
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Bootstrapping periodogram and cross periodogram statistics of vector autoregressive moving average models
(1996)Some properties of a bootstrap procedure applied to evaluate the distribution of the periodogram matrix for samples obtained from a stationary vector autoregressive moving average process are discussed.
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Bootstrapping the local periodogram of locally stationary processes
(2008)Locally stationary processes are non-stationary stochastic processes the second-order structure of which varies smoothly over time. In this paper, we develop a method to bootstrap the local periodogram of a locally stationary ...
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Estimation and testing linearity for non-linear mixed poisson autoregressions
(2015)Non-linear mixed Poisson autoregressive models are studied for the analysis of count time series. Given a correct mean specification of the model, we discuss quasi maximum likelihood estimation based on Poisson log-likelihood ...
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Extrapolation of subsampling distribution estimators: The i.i.d. and strong mixing cases
(2001)Politis & Romano (1994) proposed a general subsampling methodology for the construction of large-sample confidence regions for an arbitrary parameter under minimal conditions. Nevertheless, the subsampling distribution ...
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Frequency domain tests of semiparametric hypotheses for locally stationary processes
(2009)Many time series in applied sciences obey a time-varying spectral structure. In this article, we focus on locally stationary processes and develop tests of the hypothesis that the time-varying spectral density has a ...
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Goodness-of-fit tests for Markovian time series models: Central limit theory and bootstrap approximations
(2008)New goodness-of-fit tests for Markovian models in time series analysis are developed which are based on the difference between a fully nonparametric estimate of the one-step transition distribution function of the observed ...
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Hybrid wild bootstrap for nonparametric trend estimation in locally stationary time series
(2015)Based on consistency and asymptotic normality of a nonparametric kernel trend estimation in the context of locally stationary processes, validity of a hybrid wild bootstrap approach for estimating the distribution of the ...
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The hybrid wild bootstrap for time series
(2012)We introduce a new and simple bootstrap procedure for general linear processes, called the hybrid wild bootstrap. The hybrid wild bootstrap generates frequency domain replicates of the periodogram that imitate asymptotically ...
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Inference for the Fourth-Order Innovation Cumulant in Linear Time Series
(2015)The rescaled fourth-order cumulant of the unobserved innovations of linear time series is an important parameter in statistical inference. This article deals with the problem of estimating this parameter. An existing ...
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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 ...
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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 ...
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Local block bootstrap inference for trending time series
(2013)Resampling for stationary sequences has been well studied in the last couple of decades. In the paper at hand, we focus on nonstationary time series data where the nonstationarity is due to a slowly-changing deterministic ...