Regression Shrinkage and Selection via the Elastic Net , with Applications to Microarrays

By Hui Zou, Trevor J. Hastie. 2003

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Citations in the corpus

Interval Coded Scoring: a toolbox for interpretable scoring systems (2018, methodology): “At its introduction, the elastic net was immediately applied to microarray data due to its high dimensionality (Zou and Hastie, 2003).”
Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data (2005, background): “For genes sharing the same biological pathways, the correlations among them can be high (Zou and Hastie, 2003).”
Unified LASSO Estimation by Least Squares Approximation (2007, methodology): “To avoid the double-shrinkage effect (Zou and Hastie 2006), we set κ to be some small positive number (e.”
ELECTROENCEPHALOGRAPHIC (EEG) CONTROL OF THREE-DIMENSIONAL MOVEMENT (2010, methodology): “While the present study used stepwise regression methods to select features, alternative procedures (e.g., Tibshirani 1996, Farquhar 2009, van Gerven 2009) might prove more effective.”
Variable selection with error control: another look at stability selection (2011, unknown): “In the graphs which follow, we look at CPSS applied to the lasso (Tibshirani, 1996), which we implemented by using the package glmnet (Friedman et al., 2010) in R (R Development Core Team, 2010).”
Distribution-Free Predictive Inference for Regression (2016, methodology): “The base estimator we consider here is the lasso (Tibshirani 1996), defined by”
Forward Regression for Ultra-High Dimensional Variable Screening (2009, methodology): “, 2008), the SCAD (Fan and Li, 2001; Fan and Peng, 2004), the elastic net (Zou and Hastie, 2005; Zou and Zhang, 2008), the Adaptive LASSO (Zou, 2006; Zhang and Lu, 2007), the penalized one-step estimator (Zou and Li, 2008), and others.”
Simultaneous Variable Selection (2005, methodology): “(1)Given this high correlation between regressors, another recently proposed modification of the LASSO, the “elastic net” (Zou and Hastie, 2003) might be more appropriate if one wants to model the response variables separately.”
Efficient Water Quality Prediction Using Supervised Machine Learning (2019, methodology): “It combines the method of penalties of both methods and minimizes the loss function [25].”
A group bridge approach for variable selection (2009, methodology): “This procedure is close to Fu (1998) but also resembles the tuning parameter selection method in Tibshirani (1996) and Zhang and Lu (2006).”
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance (2013, methodology): “…degree of penalisation differs between approaches: In ridge regression λ=2 (also called “L2-norm”: Hoerl and Kennard 1970), in LASSO regression λ=1 (“L1-norm”: Tibshirani 1996) and in OSCAR (see below) λ 420 is optimised using the L1-norm together with the pairwise L∞-norm (Bondell and Reich 2007).”