Jackknifing under a Budget Constraint
STANFORD UNIV CA DEPT OF OPERATIONS RESEARCH
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This paper considers the problem of estimating a parameter alpha that can be expressed as a nonlinear function of samples means. We develop a jackknife estimator for alpha 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 pertinent to regenerative steady-state simulations.
- Statistics and Probability