发表机构
Reed College; Bucknell University; Genospace（里德学院; 巴克内尔大学; Genospace）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文在模型辅助框架下提出一种惩罚逻辑广义回归估计量，结合Lasso或Ridge惩罚控制辅助变量影响，经模拟和实例验证可提高稀疏模型下估计效率，适用于含多辅助变量的复杂调查数据场景。
AI 中文摘要
在模型辅助框架下，本文开发了一种惩罚逻辑广义回归估计量，用于从复杂调查数据和辅助数据中估计有限总体比例。该估计量通过Lasso或Ridge惩罚控制不必要辅助变量的影响。本文推导了惩罚回归系数和惩罚逻辑广义回归估计量的中心极限定理。模拟结果表明，当真实模型稀疏时，加入惩罚可提高估计量的效率。美国林务局数据的实例验证了该估计量适用于存在大量可用辅助变量的调查场景。
英文摘要
Under a model-assisted framework, a penalized logistic generalized regression estimator is developed to estimate a finite population proportion from complex survey data and auxiliary data. The proposed estimator controls the impact of unnecessary auxiliary variables through a lasso or ridge penalty. A central limit theorem is derived for the penalized regression coefficients and for the penalized logistic generalized regression estimator. Through simulations, it is shown that including the penalty increases the efficiency of the estimator when the true model is sparse. An example using United States Forest Service data demonstrates the applicability of the estimator for surveys with many available auxiliary variables.
