Estimating Covariance Matrices Using Estimating Functions in Nonparametric and Semiparametric Regression

We use ideas from estimating function theory to derive new, simply computed consistent covariance matrix estimates in nonparametric regression and in a class of semiparametric problems. Unlike other estimates in the literature, ours do not require auxiliary or additional nonparametric regressions