The Annals of Statistics

Adaptive confidence interval for pointwise curve estimation

Dominique Picard and Karine Tribouley

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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.

Article information

Ann. Statist., Volume 28, Number 1 (2000), 298-335.

First available in Project Euclid: 14 March 2002

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Zentralblatt MATH identifier

Primary: 62C20: Minimax procedures 62G07: Density estimation 62G15: Tolerance and confidence regions 26G30

Adaptive estimation Confidence interval Edgeworth expansion Wavelet methods


Picard, Dominique; Tribouley, Karine. Adaptive confidence interval for pointwise curve estimation. Ann. Statist. 28 (2000), no. 1, 298--335. doi:10.1214/aos/1016120374.

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