A penalized logistic generalized regression estimator

Abstract

Under a model-assisted framework, a penalized logistic generalized regression estimator is developed to estimate a finite population proportion from complex survey data and auxiliary data. The proposed estimator controls the impact of unnecessary auxiliary variables through a lasso or ridge penalty. A central limit theorem is derived for the penalized regression coefficients and for the penalized logistic generalized regression estimator. Through simulations, it is shown that including the penalty increases the efficiency of the estimator when the true model is sparse. An example using United States Forest Service data demonstrates the applicability of the estimator for surveys with many available auxiliary variables.