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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.

Estimating Covariance Matrices Using Estimating Functions in Nonparametric and Semiparametric Regression