Banded and tapered estimates for autocovariance matrices and the linear process bootstrap

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Exploring the sources of uncertainty: Why does bagging for time series forecasting work? (2018, methodology): “While the block bootstrap algorithms produce data that mimic the original series, LPB has been “shown to be asymptotically valid for the sample mean and related statistics” (McMurry and Politis, 2010).”
Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation (2016, methodology): “See Pang et al. (2014) for more details. For the semi-definite program (24), Yuan and Lin (2007) solved the problem using interiorpoint method for the general max-det problem which is proposed by Vanderberghe et al.”
Covariance matrix estimation and linear process bootstrap for multivariate time series of possibly increasing dimension (2015, methodology): “A recent addition to the available time series bootstrap methods was the linear process bootstrap (LPB) introduced by McMurry and Politis (2010) who showed its validity Received January 2014; revised September 2014.”