Beschreibung:
<jats:p> In this paper, we consider the problem of estimating a parameter α that can be expressed as a nonlinear function of sample means. We develop a jackknife estimator for α that is appropriate to computational settings in which the total computer budget to be used is constrained. Despite the fact that the jackknifed observations are not i.i.d., we are able to show that our jackknife estimator reduces bias without increasing asymptotic variance. This makes the estimator particularly suitable for small sample applications. Because a special case of this estimator problem is that of estimating a ratio of two means, the results in this paper are partinent to regenerative steady-state simulations. </jats:p><jats:p> INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499. </jats:p>