Nonparametric stochastic regression with design-adapted wavelets

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How researchers see this paper

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Smooth Design-Adapted Wavelets for Nonparametric Stochastic Regression (2004, methodology): “We build on the method and theory of Delouille et al. (2001), which uses the orthonormal unbalanced Haar basis (Girardi and Sweldens 1997).”
Regression in random design and warped wavelets (2004, methodology): “In the case of irregular design, various attempts to solve this problem have been studied : see for instance the interpolation methods of Hall and Turlach (1997) [25], Kovac and Silverman (2000) [30], the binning method of Antoniadis, Gregoire and Vial (1997) [2], the transformation method of Cai an”
Unbalanced Haar Technique for Nonparametric Function Estimation (2007, methodology): “UH wavelets were introduced by Girardi and Sweldens (1997) and applied in a nonparametric stochastic regression context by Delouille, Franke, and von Sachs (2001), who introduced “unbalancedness” to handle the fact that the design was stochastic and thus nonequispaced; thus the need for basis…”
Multiscale methods for data on graphs and irregular multidimensional situations (2009, methodology): “In two dimensions curve estimation with lifting has been tackled by Delouille (2002) and Delouille et al. (2003): this work and the current paper both develop and build on Jansen et al. (2001).”