mase

A set of model-assisted survey estimators and corresponding variance estimators for single stage, unequal probability, without replacement sampling designs. All of the estimators can be written as a generalized regression estimator with the Horvitz-Thompson, ratio, post-stratified, and regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6). Two of the estimators employ a statistical learning model as the assisting model: the elastic net regression estimator, which is an extension of the lasso regression estimator given by McConville et al. (2017) doi:10.1093/jssam/smw041, and the regression tree estimator described in McConville and Toth (2017) doi:10.48550/arXiv.1712.05708. The variance estimators which approximate the joint inclusion probabilities can be found in Berger and Tille (2009) doi:10.1016/S0169-7161(08)00002-3 and the bootstrap variance estimator is presented in Mashreghi et al. (2016) doi:10.1214/16-SS113.

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