DENSITY ESTIMATION WITH NORMAL MEASUREMENT ERROR WITH UNKNOWN VARIANCE

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Density estimation with heteroscedastic error (2008, background): “However, this can only be done by imposing more restrictive conditions on fX since a specific lower bound on f ft X has to be assumed (see Butucea and Matias (2005) or Meister (2006)).”
Methodology for non‐parametric deconvolution when the error distribution is unknown (2016, background): “Butucea and Matias (2005), Butucea et al. (2008) and Kneip et al. (2012) discussed problems where U has a supersmooth distribution known up to a scale parameter, and Meister (2006) addressed a similar context where U is known to be normally distributed.”
DECONVOLUTION WITH UNKNOWN ERROR DISTRIBUTION (2007, unknown): “Butucea and Matias [2] or Meister [27]).”
Density Estimation in the Presence of Heteroscedastic Measurement Error (2008, background): “Other recent progress in this area has been made by Delaigle (2007a), Delaigle, Hall, and Qiu (2006), Delaigle and Gijbels (2002, 2004), Meister (2004, 2006), and Schennach (2004).”
A ridge-parameter approach to deconvolution (2007, result): “More recent work, which also surveys earlier research, includes that of van Es, Spreij and van Zanten [29], Delaigle and Gijbels [8–10], Meister [23, 24] and Comte, Rozenholc and Taupin [6, 7].”