# An Approximate Bayesian Approach to Model-assisted Survey Estimation with Many Auxiliary Variables.

@article{Sugasawa2020AnAB, title={An Approximate Bayesian Approach to Model-assisted Survey Estimation with Many Auxiliary Variables.}, author={Shonosuke Sugasawa and Jae Kwang Kim}, journal={arXiv: Methodology}, year={2020} }

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…

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