Abstract.
The additive model \(y = s(x) + \epsilon\) is considered 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 the complete case analysis is expected to be no more suitable. A simulation experiment is reported and the different methods are compared based on their superiority with respect to the sample mean squared error. Some focus is also given on the sample variance and the estimated bias. In detail, the complete case analysis, a kind of stochastic mean imputation, a single imputation and the nearest neighbor imputation are discussed.
Similar content being viewed by others
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Nittner, T. Missing at random (MAR) in nonparametric regression - A simulation experiment. Statistical Methods & Applications 12, 195–210 (2003). https://doi.org/10.1007/s10260-003-0054-2
Issue Date:
DOI: https://doi.org/10.1007/s10260-003-0054-2