Recent developments in bootstrap methods for dependent data

Giuseppe Cavaliere, Dimitris N. Politis, Anders Rahbek, Karl B. Gregory, Soumendra N. Lahiri and Daniel J. Nordman

Journal of Time Series Analysis, 2015, vol. 36, issue 3, 442-461

Abstract: type="main" xml:id="jtsa12117-abs-0001"> Unlike with independent data, smoothed bootstraps have received little consideration for time series, although data smoothing within resampling can improve bootstrap approximations, especially when target distributions depend on smooth population quantities (e.g., marginal densities). For approximating a broad class statistics formulated through statistical functionals (e.g., LL-estimators, and sample quantiles), we propose a smooth bootstrap by modifying a state-of-the-art (extended) tapered block bootstrap (TBB). Our treatment shows that the smooth TBB applies to time series inference cases not formally established with other TBB versions. Simulations also indicate that smoothing enhances the block bootstrap.

Date: 2015
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