A comparison of different nonparametric methods for inference on additive models

In this article we highlight the main differences of available methods for the analysis of regression functions that are probably additive separable. We first discuss definition and interpretation of the most common estimators in practice. This is done by explaining the different ideas of modeling behind each estimator as well as what the procedures are doing to the data. Computational aspects are mentioned explicitly. The illustrated discussion concludes with a simulation study on the mean squared error for different marginal integration approaches. Next, various test statistics for checking additive separability are introduced and accomplished with asymptotic theory. Based on the asymptotic results under hypothesis as well as under the alternative of non additivity we compare the tests in a brief discussion. For the various statistics, different smoothing and bootstrap methods we perform a detailed simulation study. A main focus in the reported results is directed on the (non-) reliability of the methods when the covariates are strongly correlated among themselves. Again, a further point are the computational aspects. We found that the most striking differences lie in the different pre-smoothers that are used, but less in the different constructions of test statistics. Moreover, although some of the observed differences are strong, they surprisingly can not be revealed by asymptotic theory.