Asymptotic normality and confidence intervals for inverse regression models with convolution-type operators

Nicolai Bissantz and Melanie Birke

Journal of Multivariate Analysis, 2009, vol. 100, issue 10, 2364-2375

Abstract: We consider inverse regression models with convolution-type operators which mediate convolution on (d>=1) and prove a pointwise central limit theorem for spectral regularisation estimators which can be applied to construct pointwise confidence regions. Here, we cope with the unknown bias of such estimators by undersmoothing. Moreover, we prove consistency of the residual bootstrap in this setting and demonstrate the feasibility of the bootstrap confidence bands at moderate sample sizes in a simulation study.

Keywords: Bootstrap; Inverse; problems; Model; selection; Testing (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (1)

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