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A Smooth Block Bootstrap for Statistical Functionals and Time Series
Author(s) -
Gregory Karl B.,
Lahiri Soumendra N.,
Nordman Daniel J.
Publication year - 2015
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12117
Subject(s) - mathematics , resampling , smoothing , series (stratigraphy) , estimator , quantile , inference , block (permutation group theory) , statistical inference , statistics , population , algorithm , combinatorics , computer science , artificial intelligence , paleontology , demography , sociology , biology
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.