Missing at Random (MAR) in Nonparametric Regression - A Simulation Experiment

This paper considers an additive model y = f(x) + e when some
observations on x are missing at random but corresponding
observations on y are available. Especially for this model missing
at random is an interesting case because of the fact that the
complete case analysis is not expected to be suitable. A simulation
study is reported and methods are compared based on superiority
measures as the sample mean squared error, sample variance and
estimated sample bias. In detail, complete case analysis, zero
order regression plus random noise, single imputation and nearest
neighbor imputation are discussed.