mase - Model-Assisted Survey Estimators
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) <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>.
