Nonparametric estimators are introduced for the distribution of a latent linear predictor Z under unlinked observations with additive noise, achieving parametric rates in Wasserstein-1 distance independent of noise smoothness.
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Deconvolution in unlinked linear models
Nonparametric estimators are introduced for the distribution of a latent linear predictor Z under unlinked observations with additive noise, achieving parametric rates in Wasserstein-1 distance independent of noise smoothness.