Two-stage estimation for seemingly unrelated nonparametric regression models

This paper is concerned with the estimating problem of seemingly unrelated (SU) nonparametric regression models. The authors propose a new method to estimate the unknown functions, which is an extension of the two-stage procedure in the longitudinal data framework. The authors show the resulted estimators are asymptotically normal and more efficient than those based on only the individual regression equation. Some simulation studies are given in support of the asymptotic results. A real data from an ongoing environmental epidemiologic study are used to illustrate the proposed procedure.