Keywords: survey sampling, model-assisted estimation, elastic net regression, lasso
Often in natural resource surveys where the goal is to estimate population quantities, auxiliary data can supplement the survey data and improve estimator efficiency. One common technique is to build a model-assisted regression estimator where a linear model is assumed for the relationship between the survey variable and auxiliary variables. We propose estimating the coefficients of the model using elastic net regression to control for extraneous variables and multicollinearity. Using data from the Forest Inventory and Analysis Program, we compare the model-assisted estimators based on regularized regression to other survey estimators.