Adaptive wavelet estimator for nonparametric density deconvolution
Pensky, Marianna ; Vidakovic, Brani
Ann. Statist., Tome 27 (1999) no. 4, p. 2033-2053 / Harvested from Project Euclid
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The problem of estimating a density $g$ based on a sample $X_1, X_2,\dots, X_n$ from $p = q*g$ is considered. Linear and nonlinear wavelet estimators based on Meyer-type wavelets are constructed. The estimators are asymptotically optimal and adaptive if $g$ belongs to the Sobolev space $H^{\alpha}$ . Moreover, the estimators considered in this paper adjust automatically to the situation when $g$ is supersmooth.
Publié le : 1999-12-14
Classification:  Mixing distribution,  wavelet transformation,  Sobolev space,  Meyer wavelet,  62G05,  62G07 
@article{1017939249,
     author = {Pensky, Marianna and Vidakovic, Brani},
     title = {Adaptive wavelet estimator for nonparametric density
		 deconvolution},
     journal = {Ann. Statist.},
     volume = {27},
     number = {4},
     year = {1999},
     pages = { 2033-2053},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1017939249}
}

Pensky, Marianna; Vidakovic, Brani. Adaptive wavelet estimator for nonparametric density
		 deconvolution. Ann. Statist., Tome 27 (1999) no. 4, pp.  2033-2053. http://gdmltest.u-ga.fr/item/1017939249/