  This paper develops a method to construct uniform confidence bands for a
nonparametric regression function where a predictor variable is subject to a
measurement error. We allow for the distribution of the measurement error to be
unknown, but assume the availability of validation data or repeated
measurements on the latent predictor variable. The proposed confidence band
builds on the deconvolution kernel estimation and a novel application of the
multiplier bootstrap method. We establish asymptotic validity of the proposed
confidence band. To our knowledge, this is the first paper to derive
asymptotically valid uniform confidence bands for nonparametric
errors-in-variables regression.
