Bootstrap bandwidth selection in kernel density estimation from a contaminated sample

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Penalized contrast estimator for adaptive density deconvolution (2006, methodology): “The comparison is given in Table 3 between the median ISE computed for 500 samples generated with the same interval length and signal to noise ratio as Delaigle and Gijbels (2004a).”
A ridge-parameter approach to deconvolution (2007, methodology): “For recent examples, see Delaigle and Gijbels [9, 10] in the setting of kernel techniques, and Comte, Rozenholc and Taupin [6, 7] for penalization methods.”
Non‐parametric confidence bands in deconvolution density estimation (2007, methodology): “These include cross-validation procedures (Stefanski and Carroll, 1990; Hesse, 1999), bootstrap methods (Delaigle and Gijbels, 2001) and methods based on plug-in and to some extent ‘solve-the-equation’ ideas (Delaigle and Gijbels, 2004).”
Data‐driven density estimation in the presence of additive noise with unknown distribution (2011, methodology): “…(1988), Devroye (1989), Fan (1991), Liu and Taylor (1989), Masry (1991), Stefanski and Carroll (1990), Zhang (1990), Hesse (1999), Cator (2001) and Delaigle and Gijbels (2004) for mainly kernel methods, Koo (1999) for a spline method, Pensky and Vidakovic (1999) and Fan and Koo (2002) for wavelet…”