Model-assisted estimation with complex survey data is an important practical problem in survey sampling. When there are many auxiliary variables, selecting significant variables associated with the study variable would be necessary to achieve efficient estimation of population parameters of interest. In this paper, we formulate a regularized regression estimator in the framework of Bayesian inference using the penalty function as the shrinkage prior for model selection. The proposed Bayesian approach enables us to get not only efficient point estimates but also reasonable credible intervals. Results from two limited simulation studies are presented to facilitate comparison with existing frequentist methods.

中文翻译：

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具有许多辅助变量的模型辅助调查估计的近似贝叶斯方法

复杂调查数据的模型辅助估计是调查抽样中的一个重要实际问题。当有许多辅助变量时，需要选择与研究变量相关的显着变量，以实现对感兴趣的总体参数的有效估计。在本文中，我们使用惩罚函数作为模型选择的收缩先验，在贝叶斯推理的框架中制定了一个正则化回归估计器。提议的贝叶斯方法使我们不仅能够获得有效的点估计，而且能够获得合理的可信区间。提供了两个有限模拟研究的结果，以促进与现有频率论方法的比较。