A1404
Title: Agnostic model-assisted estimation in finite population sampling
Authors: Mehdi Dagdoug - McGill University (Canada) [presenting]
Ziming An - University of Ottawa (Canada)
David Haziza - University of Ottawa (Canada)
Abstract: The aim of survey sampling is to estimate finite population parameters. In many settings, auxiliary information is available at the population level and can be incorporated through modeling within the model-assisted framework. Establishing the asymptotic properties of such estimators has traditionally been carried out on a method-by-method basis and has remained an active area of research over the past 25 years. Although these estimators often share similar properties, the techniques used to analyze them are typically tailored to the specific prediction method, and a general theoretical framework applicable to arbitrary prediction methods is lacking. A novel form of conditional cross-fitting adapted to the survey sampling setting is introduced to address this gap. Leveraging this approach, a unified theory for the asymptotic behavior of model-assisted estimators based on general prediction methods is developed. In particular, the asymptotic normality of the proposed estimators and the consistency of a corresponding cross-fitting variance estimator are established, thereby enabling the construction of asymptotically valid confidence intervals. Simulation studies demonstrating the favorable finite-sample performance of the proposed methodology will also be presented.
