Statistical Learning with Sparsity: The Lasso and Generalizations

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Enumerate Lasso Solutions for Feature Selection (2017, background): “See (Hastie, Tibshirani, and Wainwright 2015) for more details of Lasso regression.”
Adaptive Huber Regression (2017, methodology): “We refer to the monographs, Bühlmann and van de Geer (2011) and Hastie, Tibshirani, and Wainwright (2015), for a systematic coverage of contemporary statistical methods for high-dimensional data.”
Causal Interaction in Factorial Experiments: Application to Conjoint Analysis (2018, unknown): “In particular, we estimate the selection probability for each AME and AMIE using one minus the proportion of 5000 bootstrap replicates in which all coe ffi cients for the corresponding factor or factor interaction are estimated to be zero (Efron 2014; Hastie, Tibshirani, and Wainwright 2015).”
Feature Selection (2016, background): “It refers to the phenomenon that data become sparser in high-dimensional space, adversely affecting algorithms designed for low-dimensional space (Hastie et al. 2005).”