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• W1543953204 abstract "The density deconvolution problem involves recovering a target density g from a sample that has been corrupted by noise. From the perspective of Le Cam's local asymptotic normality theory, we show that non-parametric density deconvolution with Gaussian noise behaves similarly to a low-dimensional parametric problem that can easily be solved by maximum likelihood. This framework allows us to give a simple account of the statistical efficiency of density deconvolution and to concisely describe the effect of Gaussian noise on our ability to estimate g, all while relying on classical maximum likelihood theory instead of the kernel estimators typically used to study density deconvolution." @default.
• W1543953204 created "2016-06-24" @default.
• W1543953204 creator A5089493027 @default.
• W1543953204 date "2015-07-03" @default.
• W1543953204 modified "2023-09-27" @default.
• W1543953204 title "The Efficiency of Density Deconvolution" @default.
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• W1543953204 hasPublicationYear "2015" @default.
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