The WaveD Transform in R: Performs Fast Translation-Invariant Wavelet Deconvolution

Autor: Marc Raimondo, Michael Stewart

Publikováno v: Journal of Statistical Software, Vol 21, Iss 2 (2007)

This paper provides an introduction to a software package called waved making available all code necessary for reproducing the figures in the recently published articles on the WaveD transform for wavelet deconvolution of noisy signals. The forward W

Externí odkaz: https://doaj.org/article/dafd743dd3d84934971c48b616ca5fe3

Kernel Methods for Optimal Change-Points Estimation in Derivatives

Autor: Marc Raimondo, Ming-Yen Cheng

Publikováno v: Journal of Computational and Graphical Statistics. 17:56-75

In this article we propose an implementation of the so-called zero-crossing-time detection technique specifically designed for estimating the location of jump-points in the first derivative (kinks) of a regression function f. Our algorithm relies on

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::90573f36150ee2d746937bb30d94663c
https://doi.org/10.1198/106186008x289164

Dicussion on the Meeting on ‘Statistical Approaches to Inverse Problems’

Autor: Eero Saksman, Dominique Picard, S Meng, F Ruymgaart, David J. Evans, Manfred Opper, G Wahba, Robert G. Aykroyd, L Cavalier, Manuel Davy, Heikki Haario, Ad Stoffelen, Sofia C. Olhede, Daniela De Canditiis, Simon J. Godsill, Felix Abramovich, Christophe Andrieu, Guy P. Nason, Patrick J. Wolfe, Markku Lehtinen, Lehel Csató, Dan Cornford, C Butucea, Eric Moulines, D Paul, Gerard Kerkyacharian, E Khabie-Zeitoune, Marko Laine, Marianna Pensky, Alexandre B. Tsybakov, Johanna Tamminen, Marc Raimondo, Robert West, Ross N. Hoffman, W Ng, U Golubev, Noel A Cressie, Iain M. Johnstone, Christian P. Robert, Axel Munk

Publikováno v: Journal of the Royal Statistical Society Series B: Statistical Methodology. 66:627-652

Johnstone, Kerkyacharian, Picard and Raimondo Johnstone, Kerkyacharian, Picard and Raimondo are interested in the inverse problem of estimating f where f has been convolved with g and then contaminated with white noise. This popular problem has been

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7d43da1a9e1908a6f46173a649f50b2d
https://doi.org/10.1111/j.1467-9868.2004.2060d.x

Wavelet regression in random design with heteroscedastic dependent errors

Autor: Marc Raimondo, Rafał Kulik

Publikováno v: Ann. Statist. 37, no. 6A (2009), 3396-3430

We investigate function estimation in nonparametric regression models with random design and heteroscedastic correlated noise. Adaptive properties of warped wavelet nonlinear approximations are studied over a wide range of Besov scales, $f\in\mathcal

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::32aa16a9d8f61c6199668f050a763d16
https://doi.org/10.1214/09-aos684

Wavelet deconvolution in a periodic setting

Autor: Gerard Kerkyacharian, Iain M. Johnstone, Dominique Picard, Marc Raimondo

Publikováno v: Journal of the Royal Statistical Society: Series B
Journal of the Royal Statistical Society: Series B, 2004, 66 n.3, pp.547-573
Journal of the Royal Statistical Society: Series B, Royal Statistical Society, 2004, 66 n.3, pp.547-573

In this paper, we present an inverse estimation procedure which combines Fourier analysis with wavelet expansion. In the periodic setting, our method can recover a blurred function observed in white noise. The blurring process is achieved through a c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3cad6adfa077f73169072e2bac4ddb67
https://hal.science/hal-00102266