Sample-split regression estimation with high dimensional covariates in survey sampling

Music City Center 

In a finite population sampling survey, model-assisted regression estimation is developed to incorporate the auxiliary information efficiently. When we have high-dimensional auxiliary data sets, adding too many auxiliary variables may increase the estimation error and lead to biased estimation. Particularly under informative sampling, the bias of the high dimensional regression estimator may not be negligible. In this paper, we present a novel application of the sample-split estimation method for regression estimation under informative sampling. The proposed method is shown to be consistent even when the auxiliary variables are high-dimensional, and the sampling design is informative. Variance estimation for the sample-split estimator is discussed. Results from a limited simulation study are also presented.

Sample-split estimation

Informative sampling

Model-assisted estimation

High-dimensional regression 

Survey Research Methods Section