• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.1-13
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• 2017

This manuscript summarized advances in bootstrap methods for long-range dependent time series data. The stationary linear long-memory process is briefly described, which is a target process for bootstrap methodologies on time-domain and frequency-domain in this review. We illustrate time-domain bootstrap under long-range dependence, moving or non-overlapping block bootstraps, and the autoregressive-sieve bootstrap. In particular, block bootstrap methodologies need an adjustment factor for the distribution estimation of the sample mean in contrast to applications to weak dependent time processes. However, the autoregressive-sieve bootstrap does not need any other modification for application to long-memory. The frequency domain bootstrap for Whittle estimation is provided using parametric spectral density estimates because there is no current nonparametric spectral density estimation method using a kernel function for the linear long-range dependent time process.

• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.899-912
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• 2016

In ATSC 3.0 systems, a bootstrap signal is located at the start of each frame. In this paper, we propose an initial synchronization scheme for ATSC 3.0 systems using the bootstrap signal. The bootstrap signal of ATSC 3.0 has several repetition patterns in the time domain. By utilizing the repetition patterns within the bootstrap, the proposed scheme can obtain an initial synchronization at the receiver. Also, simulation results show that the proposed scheme can obtain an initial synchronization at very low signal-to-noise ratios.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.121-134
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• 2006

This paper presents the resampling method for time series data in the frequency domain obtained by using discrete cosine transforms(DCT) The advantage of the proposed method is to generate bootstrap samples in time domain comparing with existing bootstrapping method. When time series are stationary, statistical properties of DCT coefficients are investigated and provide the verification of the proposed procedure.