Nonparametric comparison of regression functions

In this work, we provide a new methodology for comparing regression functions m1m1 and m2m2 from two samples. Since apart from smoothness no other (parametric) assumptions are required, our approach is based on a comparison of nonparametric estimators mˆ1 and mˆ2 of m1m1 and m2m2, respectively. The test statistics Tˆ incorporate weighted differences of mˆ1 and mˆ2 computed at selected points. Since the design variables may come from different distributions, a crucial question is where to compare the two estimators. As our main results we obtain the limit distribution of Tˆ (properly standardized) under the null hypothesis H0:m1=m2H0:m1=m2 and under local and global alternatives. We are also able to choose the weight function so as to maximize the power. Furthermore, the tests are asymptotically distribution free under H0H0 and both shift and scale invariant. Several such Tˆ’s may then be combined to get Maximin tests when the dimension of the local alternative is finite. In a simulation study we found out that our tests achieve the nominal level and already have excellent power for small to moderate sample sizes.