The convolution theorem for estimating linear functionals in indirect nonparametric regression models

DOI10.1016/j.jspi.2006.06.030zbMath1104.62041OpenAlexW2030054395MaRDI QIDQ866624

Mikail Shubov, Ali Khoujmane, Frits H. Ruymgaart

Publication date: 14 February 2007

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2006.06.030

zbMATH Keywords

local asymptotic normalityconvolution theoremindirect nonparametric regressionlinear functional of the regression function

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20)

Related Items (1)

Information bounds for inverse problems with application to deconvolution and Lévy models

Cites Work

• Speed of estimation in positron emission tomography and related inverse problems
• Robust location estimates
• Weak convergence and empirical processes. With applications to statistics
• Non-parametric applications of an infinite dimensional convolution theorem
• Asymptotic Statistics
• A characterization of limiting distributions of regular estimates
• Asymptotically efficient estimation of linear functionals in inverse regression models
• Application of convolution theorems in semiparametric models with non-i. i. d. data
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