A ridge-parameter approach to deconvolution
By Peter Hall, A. Meister. 2007
114 citations recorded by the source corpus.
Kernel methods for deconvolution have attractive features, and prevail in the literature. However, they have disadvantages, which include the fact that they are usually suitable only for cases where the error distribution is infinitely supported and its characteristic function does not ever vanish. Even in these settings, optimal convergence rates are achieved by kernel estimators only when the kernel is chosen to adapt to the unknown smoothness of the target distribution. In this paper we suggest alternative ridge methods, not involving kernels in any way. We show that ridge methods (a) do not require the assumption that the error-distribution characteristic function is nonvanishing; (b) adapt themselves remarkably well to the smoothness of the target density, with the result that the degree of smoothness does not need to be directly estimated; and (c) give optimal convergence rates in a broad range of settings.
Subfields: Artificial Intelligence, Computational Mechanics
View Paper: https://www.semanticscholar.org/paper/a2def1dedcee42981173d66f296877a8fdfae696
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computational Mechanics
Citations in the corpus
- A SPECTRAL METHOD FOR DECONVOLVING A DENSITY (2010, background): “One of the referees pointed out recent contributions by Hall and Meister (2007) and Meister (2007, 2008) that propose a similar solution to the problem of zeros.”
- Density estimation with heteroscedastic error (2008, background): “Recent contributions to density deconvolution include Zhang and Karunamuni (2000), Carroll and Hall (2004), van Es and Uh (2005), Hall and Qiu (2005) and Hall and Meister (2007).”