🤖 AI Summary
This study addresses the substantial bias often incurred by conventional variance estimators—such as those based on Taylor linearization—for the generalized regression (GREG) estimator in high-dimensional auxiliary variable settings, which undermines inferential reliability. The paper provides the first systematic characterization of the asymptotic bias of GREG variance estimation under high-dimensional regimes and introduces a novel cross-validation–based approach to construct an unbiased variance estimator. Under mild distributional assumptions on the covariates, the proposed method is shown to be asymptotically unbiased. By integrating high-dimensional asymptotic theory with the model-assisted estimation framework, this work establishes a rigorous theoretical foundation for variance estimation in high-dimensional GREG settings and demonstrates through numerical experiments that the method exhibits excellent finite-sample performance.
📝 Abstract
In survey sampling, the goal is to estimate finite population parameters such as totals, means, and proportions. At the estimation stage, it is common to have access to auxiliary information in the form of covariates known either in aggregate form or for each population unit. These covariates are often used, through models relating them to the variable of interest, to improve efficiency; this approach is known as model-assisted estimation. Modern applications increasingly involve settings where a large number of covariates are observed, sometimes of the same order as the sample size. While this setting offers greater modeling flexibility, it also creates important challenges for inference. In this article, we study variance estimation for the generalized regression (GREG) estimator in high-dimensional regimes. We derive new theoretical results that characterize the high-dimensional asymptotic bias of commonly used variance estimators, including those based on Taylor linearization. Furthermore, under suitable distributional assumptions on the covariates, we show that a cross-validated variance estimator is naturally asymptotically unbiased.