Adaptive confidence interval for pointwise curve estimation
Picard, Dominique ; Tribouley, Karine
Ann. Statist., Tome 28 (2000) no. 3, p. 298-335 / Harvested from Project Euclid
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We present a procedure associated with nonlinear wavelet methods that provides adaptive confidence intervals around $f (x_0)$, in either a white noise model or a regression setting. A suitable modification in the truncation rule for wavelets allows construction of confidence intervals that achieve optimal coverage accuracy up to a logarithmic factor. The procedure does not require knowledge of the regularity of the unknown function $f$; it is also efficient for functions with a low degree of regularity.
Publié le : 2000-02-14
Classification:  Adaptive estimation,  Confidence interval,  Edgeworth expansion,  Wavelet methods,  62C20,  62G07,  62G15,  26G30 
@article{1016120374,
     author = {Picard, Dominique and Tribouley, Karine},
     title = {Adaptive confidence interval for pointwise curve
		 estimation},
     journal = {Ann. Statist.},
     volume = {28},
     number = {3},
     year = {2000},
     pages = { 298-335},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1016120374}
}

Picard, Dominique; Tribouley, Karine. Adaptive confidence interval for pointwise curve
		 estimation. Ann. Statist., Tome 28 (2000) no. 3, pp.  298-335. http://gdmltest.u-ga.fr/item/1016120374/