Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data

By Jiang Gui, Hongzhe Li. Bioinform., 2005

549 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/815f5280fa3c080533f1bfd7891268e61efb5bff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Robert Tibshirani
2. Trevor J. Hastie
3. Jerome H. Friedman
4. Thomas H. Scheike
5. Anders Gorst-rasmussen
6. Lukas Meier
7. Noah R. Simon
8. Jelle J. Goeman

In Statistics and Probability

1. Robert Tibshirani
2. Trevor J. Hastie
3. Anders Gorst-rasmussen
4. Thomas H. Scheike
5. Daniel M. McNeish
6. Peter Buhlmann
7. Cynthia Rudin
8. Noah R. Simon

Citations in the corpus

• L1 Penalized Estimation in the Cox Proportional Hazards Model (2009, methodology): “This alternating scheme was refined by Gui and Li (2005), who suggested to use an iteratively reweighted LARS algorithm.”
• Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent (2011, methodology): “Gui and Li (2005) developed an algorithm to fit this model using Newton Raphson approximations and the lasso path solution to the penalized least squares problem provided by the adjusted LARS (least angle regression) solution of Efron et al. (2004).”
• Coordinate Descent Methods for the Penalized Semiparametric Additive Hazards Model (2012, methodology): “A range of algorithms have been developed for fitting penalized Cox models (Gui and Li, 2005; Park and Hastie, 2007; Sohn et al. , 2009; Goeman, 2010, and others) but their computational performance is limited by the use of costly Newton-Raphson iterations or similar to deal with the penalized…”
• Using Lasso for Predictor Selection and to Assuage Overfitting: A Method Long Overlooked in Behavioral Sciences (2015, background): “Lasso can be applied to a wide range of dependent variable measurement scales that encompass the generalized linear model including binary, Poisson, multinomial, and time-to-event models (Gui & Li, 2005; Tibshirani, 1996, 1997).”
• Least angle and ℓ1 penalized regression: A review (2008, background): “(15) Several authors discuss extensions of LARS to these models: generalized linear models (Lokhorst, 1999; Roth, 2004; Madigan and Ridgeway, 2004; Rosset, 2005; Park and Hastie, 2007; Keerthi and Shevade, 2007) and Cox regression (Tibshirani, 1997; Gui and Li, 2005; Park and Hastie, 2007).”