Authors: Bissantz, Nicolai
Dette, Holger
Hildebrandt, Thimo
Title: Additive inverse regression models with convolution-type operators
Language (ISO): en
Abstract: In a recent paper Birke and Bissantz (2008) considered the problem of nonparametric estimation in inverse regression models with convolution-type operators. For multivariate predictors nonparametric methods suff er from the curse of dimensionality and we consider inverse re- gression models with the additional qualitative assumption of additivity. In these models several additive estimators are studied. In particular, we investigate estimators under the random design assumption which are applicable when observations are not available on a grid. Finally, we compare this estimator with the marginal integration and the non-additive estimator by means of a simulation study. It is demonstrated that the new method yields a substantial improvement of the currently available procedures.
Subject Headings: additive models
convolution-type operators
inverse regression
Issue Date: 2013-03-14
Appears in Collections:Sonderforschungsbereich (SFB) 823

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