  We survey classical kernel methods for providing nonparametric solutions to
problems involving measurement error. In particular we outline kernel-based
methodology in this setting, and discuss its basic properties. Then we point to
close connections that exist between kernel methods and much newer approaches
based on minimum contrast techniques. The connections are through use of the
sinc kernel for kernel-based inference. This `infinite order' kernel is not
often used explicitly for kernel-based deconvolution, although it has received
attention in more conventional problems where measurement error is not an
issue. We show that in a comparison between kernel methods for density
deconvolution, and their counterparts based on minimum contrast, the two
approaches give identical results on a grid which becomes increasingly fine as
the bandwidth decreases. In consequence, the main numerical differences between
these two techniques are arguably the result of different approaches to
choosing smoothing parameters.
