Orthogonal decomposition of finite population statistics and its applications to distributional asymptotics

Abstract

We study orthogonal decomposition of symmetric statistics based on samples drawn without replacement from finite populations. Several applications to finite population statistics are given: we establish one-term Edgeworth expansions for general asymptotically normal symmetric statistics, prove an Efron-Stein inequality and the consistency of the jackknife estimator of variance. Our expansions provide second order a.s. approximations to Wu's jackknife histogram.