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… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 46 REFERENCES
Approximate Bayesian inference under informative sampling
Summary Statistical inference with complex survey data is challenging because the sampling design can be informative, and ignoring it can produce misleading results. Current methods of Bayesian
Model-Assisted Survey Estimation with Modern Prediction Techniques
This paper reviews the design-based, model-assisted approach to using data from a complex survey together with auxiliary information to estimate finite population parameters. A general recipe for
Variable selection for regression estimation in finite populations
The selection of auxiliary variables is considered for regression estimation in finite populations under a simple random sampling design. This problem is a basic one for model-based and
Nonparametric Model Calibration Estimation in Survey Sampling
Calibration is commonly used in survey sampling to include auxiliary information at the estimation stage of a population parameter. Calibrating the observation weights on population means (totals) of
Model-Assisted Estimation for Complex Surveys Using Penalized Splines
Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on penalised spline regression is proposed. These estimators are weighted
Model-Assisted Survey Regression Estimation with the Lasso
In the U.S. Forest Service’s Forest Inventory and Analysis (FIA) program, as in other natural resource surveys, many auxiliary variables are available for use in model-assisted inference about finite
Bayesian Inference for the Finite Population Total from a Heteroscedastic Probability Proportional to Size Sample
We study Bayesian inference for the population total in probability-proportional-to-size (PPS) sampling. The sizes of non-sampled units are not required for the usual Horvitz-Thompson or Hajek
A Model-Calibration Approach to Using Complete Auxiliary Information From Survey Data
Suppose that the finite population consists of N identifiable units. Associated with the ith unit are the study variable, yi, and a vector of auxiliary variables, xi. The values x1, x2,…, xN are
Calibration Estimators in Survey Sampling
Abstract This article investigates estimation of finite population totals in the presence of univariate or multivariate auxiliary information. Estimation is equivalent to attaching weights to the
Local polynomial regresssion estimators in survey sampling
Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on local polynomial regression is proposed. Like generalized regression
...
...