Suppose that under a semiparametric setting an estimator of a vector of par
ameters of interest is obtained by optimising an objective function which h
as a U-process structure. The covariance matrix of the estimator is general
ly a function of the underlying density function, which may be difficult to
estimate well by conventional methods. In this paper, we present a simple
resampling method by perturbing the objective function repeatedly. Inferenc
es of the parameters can then be made based on a large collection of the re
sulting optimisers. We illustrate our proposal by three examples with a het
eroscedastic regression model.