All Times EDT
Keywords: R package, survey estimation, model assisted statistics, spatial estimation, model visualization
Abstract: R package mase is 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. 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), and the regression tree estimator described in McConville and Toth (2017). The variance estimators which approximate the joint inclusion probabilities can be found in Berger and Tille (2009) and the bootstrap variance estimator is presented in Mashreghi et al. (2016).
View current poster design at: http://people.reed.edu/~irisrose/mase.pdf
About the instructor: Iris Griffith is a graduating Statistics student from Reed College. Iris is particularly interested in the fields of machine and deep learning for social good and has spent time in 2019 researching applications of ML methods in forestry data science with Dr. Kelly McConville and the US Forest Service's FIA program.