Window Subsampling of Estimating Functions with Application to Regression Models
提出一种窗口子抽样方法，用于估计方程解的渐近标准误，适用于时间或空间相关数据，无需重新估计参数，为大型数据集提供了一种优于刀切法的替代方案。
Abstract We propose a subsampling method for estimating the asymptotic standard error of a statistic β n that is the solution to an estimating equation 1/n Σn j=1 Uj(Y j Xj, β) = 0 where the data Yj may be temporally or spatially correlated and the estimating function may depend on covariates Xj . A key statistic that we consider in detail is a generalized linear model regression coefficient computed under the assumption of independence. The availability of a consistent variance estimator allows semiparametric regression approaches for clustered and longitudinal data to be used with time series and spatial data. The methods that we develop extend the subsampling ideas of Carlstein, Sherman, and Garcia-Soidan and Hall to estimating functions. Our approach provides an attractive alternative to the jackknife method of Lele, particularly for large datasets, because we do not require parameter reestimation.