Regression is commonly used to describe and analyze the relation between explanatory input variables X and one or multiple responses Y. In many applications such relations are too complicated to model with a parametric regression function. Classical nonparametric regression (see e.g., Fan and Gijbels, 1996;Wand and Jones, 1995; Loader, 1999; Simonoff, 1996) and varying coefficient models (see e.g., Hastie and Tibshirani, 1993; Fan and Zhang, 1999; Carroll et al., 1998; Cai et al., 2000), allow for a more flexible form. In this article we describe an approach that allows us to efficiently handle discontinuities and spatial inhomogeneities of the regression function in such models.
CITATION STYLE
Polzehl, J., & Spokoiny, V. (2008). Structural Adaptive Smoothing by Propagation–Separation Methods. In Handbook of Data Visualization (pp. 471–492). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-33037-0_19
Mendeley helps you to discover research relevant for your work.