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  "Title": "Model-Assisted Survey Estimators",
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  "Description": "A set of model-assisted survey estimators and\ncorresponding variance estimators for single stage, unequal\nprobability, without replacement sampling designs.  All of the\nestimators can be written as a generalized regression estimator\nwith the Horvitz-Thompson, ratio, post-stratified, and\nregression estimators summarized by Sarndal et al. (1992,\nISBN:978-0-387-40620-6). Two of the estimators employ a\nstatistical learning model as the assisting model: the elastic\nnet regression estimator, which is an extension of the lasso\nregression estimator given by McConville et al. (2017)\n<doi:10.1093/jssam/smw041>, and the regression tree estimator\ndescribed in McConville and Toth (2017) <arXiv:1712.05708>. The\nvariance estimators which approximate the joint inclusion\nprobabilities can be found in Berger and Tille (2009)\n<doi:10.1016/S0169-7161(08)00002-3> and the bootstrap variance\nestimator is presented in Mashreghi et al. (2016)\n<doi:10.1214/16-SS113>.",
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