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Generalised regression estimation via the bootstrap

机译：通过自举的广义回归估计

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摘要

A generalised regression estimation procedure is proposed that can lead to much improved estimation of population characteristics, such as quantiles, variances and coefficients of variation. The method involves conditioning on the discrepancy between an estimate of an auxiliary parameter and its known population value. The key distributional assumption is joint asymptotic normality of the estimates of the target and auxiliary parameters. This assumption implies that the relationship between the estimated target and the estimated auxiliary parameters is approximately linear with coefficients determined by their asymptotic covariance matrix. The main contribution of this paper is the use of the bootstrap to estimate these coefficients, which avoids the need for parametric distributional assumptions. First-order correct conditional confidence intervals based on asymptotic normality can be improved upon using quantiles of a conditional double bootstrap approximation to the distribution of the studentised target parameter estimate.
机译：提出了一种广义回归估计程序，这可以导致大量改善群体特征的估计，例如定量，差异和变异系数。该方法涉及调节辅助参数的估计与其已知人口值之间的差异。关键分布假设是目标和辅助参数的估计的关节渐近常态。该假设意味着估计目标与估计的辅助参数之间的关系大致线性，其渐变协方差矩阵确定的系数。本文的主要贡献是使用引导程序来估计这些系数，这避免了对参数分布假设的需求。在使用条件双向自动启动近似的定量与学生的目标参数估计的分布的分量，可以改善基于渐近正常性的一阶的正确条件置信区间。

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